This paper explores recent achievements and novel challenges of the annoying privacy-preserving big data stream mining problem, which consists in applying mining algorithms to big data streams while ensuring the privacy of data. Recently, the emerging big data analytics context has conferred a new light to this exciting research area. This paper follows the so-depicted research trend.

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Customer Relationship Management (CRM) is currently the fastest growing sector of enterprise software, estimated to increase to $36.5B worldwide by 2017. CRM technologies increasingly use data mining primitives across multiple applications. At the same time, the growth of big data has led to the evolution of an open source big data software stack (primarily powered by Apache software) that rivals traditional enterprise database (RDBMS) stacks. New technologies such as Kafka, Storm, HBase have significantly enriched this open source stack, alongside more established technologies such as Hadoop MapReduce and Mahout. Today, enterprises have a choice to make regarding which stack they will choose to power their big data applications. However, there are no published studies in literature on enterprise big data pipelines built using open source components supporting CRM. Specific questions that enterprises have include: how is the data processed and analyzed in such pipelines? What are the building blocks of such pipelines? How long does each step of this processing take? In this work, we answer these questions for a large scale (serving over a 100M customers) industrial CRM pipeline that incorporates data mining, and serves several applications. Our pipeline has, broadly, two parts. The first is a data gathering part that uses Kafka, Storm, and HBase. The second is a data mining part that uses Mahout and Hadoop MapReduce. We also provide timings for common tasks in the second part such as data preprocessing for machine learning, clustering, reservoir sampling, and frequent itemset extraction.

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This paper presents a temporal pattern mining method for medical data. It modifies the mining algorithms proposed by Batal et al. to incorporate with ranged relations. Experimental results demonstrate that the proposed method could generate frequent patterns with abstracted time ranges embedded in their temporal relations.

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Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey.

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Networks (i.e., graphs) appears in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present several new data models, whose key idea is to leverage networks as context to connect different data sets or different data mining models, including a network of networks (NoN) model, a network of co-evolving time series (NoT) model and a network of regression model. Second, we will present some algorithmic examples on how to perform mining with such new models where the key idea is to leverage the contextual network as an effective regularizer during the mining process, including ranking, imputation, prediction and inference. Finally, we will demonstrate the effectiveness of our new models and algorithms in some applications, including bioinformatics, sensor networks, critical infrastructure networks and scholarly data mining.

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In recent years, data mining and text mining techniques have been frequently used for analyzing questionnaire and review data. Data mining techniques such as association analysis and cluster analysis are used for marketing analysis, because those can discover relationships and rules hiding in enormous numerical data. On the other hand, text mining techniques such as keywords extraction and opinion extraction are used for questionnaire or review text analysis, because those can support us to investigate consumers opinion in text data. However, data mining tools and text mining tools cannot be used in a single environment. Therefore, a data which has both numerical and text data is not well analyzed because the numerical part and text part cannot be connected for interpretation. In this paper, a mining framework that can treat both numerical and text data is proposed. We can iterate data shrink and data analysis with both numerical and text analysis tools in the unique framework. Based on experimental results, the proposed system was effectively used to data analysis for review texts.

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To gain valuable information from the large number of the competition data accumulated these years and give the constructional idea on data mining process model of college students' competition. We give constructive thoughts of college competition data mining process model based on relevant data mining theory and character of the data. This process model of data mining can provide macroscopic direction and engineering method for later competition data mining project.

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Modern big data platforms such as Apache Hadoop and Apache Spark are able to process and analyse huge data sets, but still lack comprehensive support for spatial data analysis. Nevertheless, spatial data mining requires an efficient distributed processing of big spatial data. Spatial data mining is a subclass of data mining, which mainly focuses on obtaining explicit knowledge, spatial relations and interesting patterns from spatial data. Co-location pattern mining is one of the spatial data mining challenges. Spatial co-location pattern could be defined as a set of spatial objects or relationships which are frequently observed together in a spatial proximity. This work is mainly focused on development of a framework for co-location patterns mining in big spatio-temporal data. We also make evaluation of applied algorithms from the point of their efficiency and scalability.

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Data mining technology is the key technology and core content of big data age. The undergraduate data mining course introduces the basic concepts, basic principles and application techniques of data mining, as well as the characteristics and new technologies of data mining under the background of big data. According to the characteristics of undergraduate students, the curriculum should weaken the theory and algorithm as much as possible, and emphasizing the application. Through analysis and experiment on various examples, to enable students to face the specific application problems, can use the SPSS Modeler to designing a data processing, select the appropriate data mining method, and finally get the ideal results of data mining.

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This paper presented a frequent pattern mining algorithm SSDTreeMiner used in semi structured data streams. The algorithm gets the frequent pattern in the target time by using the adding and deleting principle and maintaining the sliding windows, at the same time, uses the time attenuation model to eliminate the influence of historical data in the mining process, compared with the traditional algorithms, the SSDTreeMiner algorithm can deal with high speed and complex data streams and complete mining tasks real-timely and efficiently.

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The primary objective of this paper is to present detailed analysis of various platforms suitable for Big Data processing. In this paper, various software frameworks available for Big Data analytics are surveyed and in-detail assessment of their strengths and weaknesses is discussed. In addition to this, widely used data mining algorithm are discussed for their adaptation for Big Data analysis w.r.t their suitability for handling real-world application problems. Future trends of Big Data processing and analytics can be predicted with effective implementation of these well established and widely used data mining algorithms by considering the strengths of software frameworks and platforms available. Hybrid approaches (integration of two or more platforms) may be more appropriate for a specific data mining algorithm and can be highly adaptable as well as perform real-time processing.

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These days' data mining is an emerging trend, which is presently used in different areas especially in student educational and learning analytics. It is very hard and time consuming to analyze data and finding the hidden information manually. To improvise educational data mining, clustering will be used in the paper. As we need to improvise performance as well as unambiguousness of obtained models. We have used 84 under-graduate student data and grouped students according to their final marks they achieved in the course and this we have done by using clustering approach. The result which we get shows that the clarity of specific model is much better than the general model and the unambiguousness of the model is also increase.

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Today data mining is widely used by companies with a strong consumer focus like retail, financial, communication and marketing organizations. Here technically data mining is the process of extraction of required information from huge databases. It allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. The ultimate goal of this paper is to propose a methodology for the improvement in DB-SCAN algorithm to improve clustering accuracy. The proposed improvement is based on back propagation algorithm to calculate Euclidean distance in the dynamic manner. Also this paper shows the obtained results of implemented proposed and existing methods and it compares the results in terms of its execution time and accuracy.

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Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results by calculating the accuracy, precision, recall, and F-measure values of the results. Moreover, we implement a feature selection method on the data and analyze the results with previous ones. The results will lead companies to predict their employees' churn status and consequently help them to reduce their human resource costs.

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Campus card system in generated a lot of data during it's operation, and the system itself cannot analyze these data. How it can be learned from these massive, outdated data for student management to assist decision-making becomes a very realistic subject. This paper takes the transaction data of campus card as the research object, and uses the comprehensive application of data warehouse, online analysis and processing, data mining and so on to excavate useful information hidden in campus card transaction. This paper first introduces the data warehouse, data mining technology, then it analyzes the consumption behavior of the student canteen, and then builds the data warehouse model according to the analysis of the theme, sets the dimension, selects the granularity, from the card business system we extract the appropriate data for cleaning, conversion and loading into the data warehouse, finally, through the data mining technology we analyze the data to find the rules for the school decision-makers and provide the decision support for the relevant management departments.

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Social networks are no longer a place where you can spend leisure time and chat with friends. It is also a business instrument in work with their audiences to increase brand recognition, total result from marketing and move sales up. For this purposes it's needed to make thorough analysis of the target audience, scan dozens of user profiles, reveal their interests, positions and estimate users LTV. Automation of this work using data mining methods to handle large amounts of data through pre-processing, data analysis and interpretation. In this article, we are considering suitable solutions to solve these tasks.

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Networks naturally capture a host of real-world interactions, from social interactions and email communication to brain activity. However, graphs are not always directly observed, especially in scientific domains, such as neuroscience, where monitored brain activity is often captured as time series. How can we efficiently infer networks from time series data (e.g., model the functional organization of brain activity as a network) and speed up the network construction process to scale up to millions of nodes and thousands of graphs? Further, what can be learned about the structure of the graph data? How can we automatically summarize a network 'conditionally' to its domain, i.e., summarize its most important properties by taking into account the properties of other graphs in that domain (e.g., neuroscience)? In this talk I will present our recent work on scalable algorithms for inferring, summarizing and mining large collections of graph data coming from different sources. I will also discuss applications in various domains, including connectomics and social science.

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Summary form only given. The extremely fast grow of Internet Services, Web and Mobile Applications and advance of the related Pervasive, Ubiquity and Cloud Computing concepts have stimulated production of tremendous amounts of data partially available online (call metadata, texts, emails, social media updates, photos, videos, location, etc.). Even with the power of today's modern computers it still big challenge for business and government organizations to manage, search, analyze, and visualize this vast amount of data as information. Data-Intensive computing which is intended to address this problems become quite intense during the last few years yielding strong results. Data intensive computing framework is a complex system which includes hardware, software, communications, and Distributed File System (DFS) architecture. Just small part of this huge amount is structured (Databases, XML, logs) or semi-structured (web pages, email), over 90% of this information is unstructured, what means data does not have predefined structure and model. Generally, unstructured data is useless unless applying data mining and analysis techniques. At the same time, just in case if you can process and understand your data, this data worth anything, otherwise it becomes useless. Two key components of any Data-intensive system are: Data Storage and Data Processing. So, which technologies, techniques, platforms, tools are best for Big Data storing and processing? How Big Data Era effect technological landscape? These and many other questions will be answered during a speech.

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Data analysis performed with the use of data mining methods is nowadays very current in the field of industry. The results of this data analysis can bring new knowledge which can be applicable to a given manufacturing process and thus contribute to streamlining process management. This paper aims at the data analysis of failures emerging in the industrial process with the use of data mining methods. The reference data used for the data analysis were collected from the real production process in the automotive industry area. The first part of this paper is devoted to the process of understanding the collected data and the data structure, as well as the production process itself. Next part of this paper deals with the data analysis which was performed using appropriate data mining methods. The results of these data analyzes are summarized and represented in the last part of this paper.

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With the advancement of Information technologies and applications, a copious amount of data is generated, which attracts both the research community to utilize this information for extracting knowledge and the industry for developing the knowledge-based system. Visualization of data, pattern mining from datasets and analyzing data drift for the different features are three highly used applications of machine learning and data science fields. A generic web-based tool integrated with such features will provide prodigious support for preprocessing the dataset and thus extracting accurate information. In this work, we propose such a data visualization tool, named VIM, which is a web-based comprehensive tool for generic data visualization, data preprocessing and mining suitable knowledge with drift analysis of data. Given a dataset, it can envisage the distribution of data with convenient statistical diagrams for different selected features. Moreover, users can employ VIM to generate association rules by selecting multiple features. We have developed VIM using Python Django framework and GraphLab library.

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Traditional data mining usually deals with data from a single domain. In the big data era, we are facing a diversity of datasets from different sources in different domains. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale and density. Big data have volumes in range Exabyte's ten to the power of eighteen. A large number of data's are stored in Big Data storage every second. For instance in YouTube for every second a video of size 72 hours are being uploaded. It shows that big data have a big scope in handling of large data. Big data for learning, intelligence, data fusion, social network, mining and so many plays a vital role in it. The big data technologies along with machine learning algorithm have developed lots of advanced development in the field of social mining, network and social Medias. It has also developed so many challenges in data storage, handling, representation, mining, analysing the user behaviours and so many. In Social mining along with text the symbols are also analysed for effective mining of users. This paper does not only introduce high-level principles of each category of methods, but also give examples in which these techniques are used to handle real big data problems. The data storage size can be optimized by using the map reduce algorithm in a effective way for data storage in big data. When the repeateddata's are replaced with its reference then the storage will be optimized. Thus this method implementation will leads to the effective data mining and the analysing of persons behaviour more effectively.

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Graph data management and mining in HPC environments has been a widely discussed issue in recent times. In this talk I will describe the use of Partitioned Global Address Space languages for graph data mining and management. I will first discuss the rationale behind X10 based graph libraries and graph database benchmarks using ScaleGraph and XGDBench as examples. Next, I will take Acacia which is completely developed with X10 language as an example system and describe our experience with implementing such high performance system with X10. In this talk I will describe how RDF processing and Streaming extensions have been implemented in Acacia. Finally, I will highlight some of the notable areas which need further attention in future.

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Due to large scale and complexity of big data, mining the big data using a single personal computer is a difficult problem. With increasing in the size of databases, parallel computing systems can cause considerable advantages in the data mining applications by means of the exploitation of data mining algorithms. Parallelization of association rule mining algorithms is an important task in data mining to mine frequent patterns from transaction databases. These algorithms either distribute database horizontally or increase number of CPU to reduce execution time of frequent pattern mining. In this paper, a novel frequent itemset mining algorithm, namely Horizontal parallel-Apriori (HP-Apriori), is proposed that divides database both horizontally and vertically with partitioning mining process into four sub-processes so that all four tasks are performed in parallel way. Also the HP-Apriori tries to speed up the mining process by an index file that is generated in the first step of algorithm. The proposed algorithm has been compared with Count Distribution (CD) in terms of execution time and speedup criteria on the four real datasets. Experimental results demonstrated that the HP-Apriori outperforms over CD in terms of minimizing execution time and maximizing speedup in high scalability.

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In this paper we propose a framework and approach to model events as elements of data stream and perform analysis to group values of attribute similar to each other within an attribute and find associations between clusters of values across two attributes. Experiments have been performed on both synthetic and real data sets.

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The rapidly increasing attention to customer behavior and satisfaction by department stores and commercial companies and development in social media as well as online systems has promoted production and research in user engagement pattern recognition, user network analysis, topic detection from customer feedback, text-based sentiment analysis, etc. With the development of Internet of Things and social media network, ethics consideration has also playing an important role in application of big data, especially customer behavior and feedback mining. Our proposed novel system, which extracts the important topics or issues from Skype customer feedback sources and measures the emotion associated with those topics using Vibe metric, can be a good example in this area. Unlike other previous research, which has focused on extracting the user sentiments either globally or in separate topics, our work focuses on tracking the correlated emotional trajectories across all the important issues from Skype customer feedback over time. Moreover, it also provides a platform for both studying customer emotions and tracking how the emotions regarding different important topics correlatively change over time by leveraging unstructured textual customer feedback data and structured user activity telemetry data.

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Student's academic performance is the main focus of all educational institutions. Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. The obtained results of the different FS algorithms and classifiers will also help the new researchers in finding the best combinations of FS algorithms and classifiers. Selecting relevant features for student prediction model is very sensitive issue for educational stakeholders, as they have to take decisions on the basis of results of prediction models. Furthermore our paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

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Digital libraries can provide information services for users with diverse needs. Due to a large amount of data that exists in digital library systems, including text and multimedia resources, with different cohorts of users, and the challenges with existing digital library systems in terms of maintaining privacy and confidentiality, it is very difficult to provide personalised library services and improved user experience. However, novel data mining algorithms based on automatic user segmentation and borrowing behaviour modelling can leverage the relationship between users and borrowing records, to improve the library services. In this paper, we present an automatic approach for personalising the resources by segmenting the users and their preferences, based on a data mining strategy, involving, the classification based on Naive Bayes, J48 and K-Nearest Neighbours Classification (K-NN) and using open source technology tools for evaluating the personalisation and improved user experience with digital library services.

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In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is proposed for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful applications in the past years, it is worth to further explore the opportunity to apply PIP in time series “Big Data”. However, the performance of PIP identification is always considered as the limitation when dealing with “Big” time series data. In this paper, two improvement algorithms namely Caching and Splitting algorithms are proposed. Significant improvement in term of speed is obtained by these improvement algorithms.

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TCM is a traditional medicine in China and has made great contributions to the Chinese nation and has rich information resources. With the development of information technology, data mining technology is rapidly rising. Under the guidance of Chinese medicine theory, how to combine data mining technology with Chinese medicine to make it serve people has become a new topic. This paper mainly applies association rules algorithm with TCM, and fully relies on the resources of TCM to realize the intelligent diagnosis of Chinese medicine. It provides reference for the clinical treatment and the teaching of Chinese medicine.

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Today in computer world huge amount of data is generated every second. This generated data is so huge that it is difficult to find the relevant data for a specific purpose on demand. Data mining is an approach to provide a way to extract the purpose specific data in the relevant time for the decision-making purpose. Data mining include the various process such as data cleaning, data selection, data integration, data transformation and pattern evaluation. Data mining also include several functionalities such as characterization, discrimination, association analysis, classification, prediction, clustering, evolution and deviation analysis. These all processes are depending greatly on mathematical functions and foundation. The mathematics can be seen as the tool for data mining. These mathematical functions can be combined, changed and refined to produce a more efficient algorithm for the classification of data sets. The data classification is done by the classifier also based on function on which the whole algorithmic efficiency is dependent.

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Anomaly detection is extensively renowned as a significant stage in intrusion detection system (IDS). IDS can be classified into signature detection and anomaly detection. Signature-based IDS are effective in discovering known attacks, but does not detect the newly invented attacks. They cannot adapt to evolving network data streams in real time. Also, detection rate and false alarm rate are happens to be the most vital issues and challenges in designing effective IDS. The aim of this paper is to detect the anomaly packets. To do so, this paper proposed framework by using data mining algorithm to detect anomalies. The proposed framework uses data mining techniques with sliding window model and clustering techniques. Data mining algorithms are used for mine pattern which are frequent in the network data flow. Normal traffic (profiles) compared with real time network flow to find out anomalous traffic. The effectiveness of the proposed model demonstrates with the experiment on real datasets from the various network flow. Result of the experimental show that algorithms are competent. So, the system using real Internet traffic has highlighted its ability with a high detection rate and preserving very low false alarm rate. This proposed framework provides a more accurate, effective and systematic approach to detect anomalies.

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Breast cancer is the most common cancer in women and thus the early stage detection in breast cancer can provide potential advantage in the treatment of this disease. Early treatment not only helps to cure cancer but also helps in its prevention of its recurrence. Data mining algorithms can provide great assistance in prediction of earl y stage breast cancer that always has been a challenging research problem. The main objective of this research is to find how precisely can these data mining algorithms predict the probability of recurrence of the disease among the patients on the basis of important stated parameters. The research highlights the performance of different clustering and classification algorithms on the dataset. Experiments show that classification algorithms are better predictors than clustering algorithms. The result indicates that the decision tree (C5.0) and SVM is the best predictor with 81% accuracy on the holdout sample and fuzzy c-means came with the lowest accuracy of37% among the algorithms used in this paper.

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Mining frequent itemsets from transactional data streams has been vastly studied in literature. The existing algorithms mine frequent itemsets within the stream's constrained environment of limited time and memory. However, none of them are capable of handling varying inter-arrival rates of streams. Moreover, these algorithms are not capable of giving mining results instantaneously, even with compromised accuracy if required, and improve the accuracy with increase in time allowance. These two properties characterize an anytime algorithm. In this paper, we propose AnyFI, which is the first anytime frequent itemset mining algorithm for data streams. We also propose a novel data structure, BFI-forest, which is capable of handling transactions with varying inter-arrival rate. AnyFI maintains itemsets in BFI-forest in such a way that it can give a mining result almost immediately when time allowance to mine is very less and can refine the results for better accuracy with increase in time allowance. Our experimental results show that AnyFI can handle high stream speeds upto 60,000 transactions per second (tps) with recall close to 100%.

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Prediction of agriculture yield is a job that requires unification of knowledge from several areas such as data mining, statistics and agriculture. Subject of crop yield prediction has been very popular among various organizations working in agriculture, producers etc. P rediction of crop yield helps in managing the storage of crops as well as it directs the transportation decisions, and risk management issues related to crops. Pattern of rainfall and temperature aredynamic due to global warming, and resulting in undergoing impingement on crop productivity. Data Mining focuses upon methodologies for extracting useful knowledge from data and there are several tools to extract the knowledge that is it is a proficiency of examining the dataset such that the end results can be deduced easily and rapidly from the dataset. The knowledge gathered can be used ο forecast the paddy yield. But farmers do not use any knowledge discovery process approach on paddy yield data. Data mining can be used in agriculture for decision making. In this study, we collected data from different government organizations, after preprocessing and discretization of data applied Predictive Apriori algorithm using Data Mining tool (WEKA) for analysis of daily temperature, daily rainfall and paddy yield to predict the paddy yield and to analyze the effect of temperature and rainfall on the paddy yield.

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Data mining is an extraction of useful information from the database. Data mining techniques can be applied for many applications such as in banking sector, Telecom & agricultural field. DM techniques in telecom field research has drastically increased throughout the years and many improvements in telecom field continue to be made, In this field occurring the number of new and more complex problems can apply the DM techniques. In this paper we present how the DM techniques can be applied in telecom sector, how it affects and beneficial to all customers and how these techniques can be useful for the telecom field to improve their service and generating more than the expected revenue.

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Data Mining is exceptionally helpful when it comes to educational field particularly while examining students' conduct in internet learning environment. This is because of the capability of data mining in breaking down and revealing the shrouded data itself which is tough and exceptionally tedious if only manual work is there without automation. Adoption of numerous techniques and tools of data mining is done by almost all the industries for generating business intelligence so as to improve the process of decision making. And as we come to the educational fields, they also begin to use techniques of data mining, so that they can provide improved services and increase grades and performance of student. The main principle of this paper is to focus on the application area of EDM, where we can apply e-learning.

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In order to solve the problem of data mining in big data, this paper studies the data mining engine based on big data. Using Spark as the engine core and programming model, some parallel data mining algorithms are designed and implemented, and an efficient data mining engine system is built. Therefore, the traditional data mining algorithms can run in parallel in the cluster environment, in which big data can be made better of use. Through the above work, a complete big data mining system is realized, which provides an efficient and easy-to-use tool for the implementation of data mining algorithms on big data sets.

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When studies in literature are examined, it is seen that different approaches have been used to solve facility layout problems. The relationship between departments in layout is always important. In this study, data mining technique is used for analyzing relations among departments and then association rules are obtained. Determining closeness relationships between the departments in facility are often ambiguous and require expert opinions. In such cases, a fuzzy component emerges in the facility layout problem. Hence, fuzzy logic is widely used to address ambiguous problems. Association rules is converted by using defuzzification approach to crisp values used facility layout problem solution in this study. Facility layout problems are considered to be NP-Hard (Nondeterministic-Polynomial-Hard) optimization problems. That is, definite solution approaches are limited in solving large-scale problem examples. For heuristic approaches are frequently used to improve the layout, simulated annealing approach is used in this study. To improve facility layout planning, simulated annealing approach is carried out via code written in Visual Basic 2012. In conclusion, 17% improvement is achieved with alternative layout plan obtained.

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Stationery Company located in Banjarmasin, is a company that sells items such as stationery, office equipment, and others. Business processes performed by this company are ordering and purchasing goods from suppliers and selling goods to the customer. Based on the existing situation required the completion of the method by using Data Mining association rules and administrative information systems based website for the company to be computerized and improve the control of business processes in the company. Based on the existing situation, namely the completion of the necessary sales information systems based data mining for the company to be computerized and improve the control of business processes in the company. The end result of making this application is that it can make the process of purchasing, sales, and process of data mining market basket analysis methods. Additional features to support existing data as a reminder of stock items, inventory recommendations are also included.

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India is a country where agriculture and agriculture related industries are the major source of living for the people. Agriculture is a major source of economy of the country. It is also one of the country which suffer from major natural calamities like drought or flood which damages the crop. This leads to huge financial loss for the farmers thus leading to the suicide. Predicting the crop yield well in advance prior to its harvest can help the farmers and Government organizations to make appropriate planning like storing, selling, fixing minimum support price, importing/exporting etc. Predicting a crop well in advance requires a systematic study of huge data coming from various variables like soil quality, pH, EC, N, P, K etc. As Prediction of crop deals with large set of database thus making this prediction system a perfect candidate for application of data mining. Through data mining we extract the knowledge from the huge size of data. This paper presents the study about the various data mining techniques used for predicting the crop yield. The success of any crop yield prediction system heavily relies on how accurately the features have been extracted and how appropriately classifiers have been employed. This paper summarizes the results obtained by various algorithms which are being used by various authors for crop yield prediction, with their accuracy and recommendation.

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For the quality of the wine big data identification technology, the introduction of data mining classification algorithm, effectively according to the content of several impact compounds in wine level identification;Are introduced including the Logistic regression and BP neural network and SVM classification algorithm, in view of the three algorithms identify the modeling analysis of wine quality. Data mining is closely related to big data, applying data mining to the wine in the quality detection of big data, can quickly to the quality of the wine.

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Bitcoin, one major virtual currency, attracts users' attention by its novel mode in recent years. With blockchain as its basic technique, Bitcoin possesses strong security features which anonymizes user's identity to protect their private information. However, some criminals utilize Bitcoin to do several illegal activities bringing in great security threat to the society. Therefore, it is necessary to get knowledge of the current trend of Bitcoin and make effort to de-anonymize. In this paper, we put forward and realize a system to analyze Bitcoin from two aspects: blockchain data and network traffic data. We resolve the blockchain data to analyze Bitcoin from the point of Bitcoin address while simulate Bitcoin P2P protocol to evaluate Bitcoin from the point of IP address. At last, with our system, we finish analyzing its current trends and tracing its transactions by putting some statistics on Bitcoin transactions and addresses, tracing the transaction flow and de-anonymizing some Bitcoin addresses to IPs.

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The data structure course is very important for information communication technology learning. However, some students can learn data structures well, but most students feel difficult to understand the contents of the data structure course. Therefore, a data structure exercise system applying self-regulated learning mechanism was used for students to learn by their own goals and strategies, and this study employed the classification technique to mine the relation between the prior knowledge and the effectiveness of the self-regulated learning in the data structure course. The mining results found that (1) the higher prior knowledge learners have the higher system using attitude. (2) In the first post-test, the higher prior knowledge learners have the great learning effectiveness. But there are no significant differences in the second post-test. (3) In the second post-test, the higher learning effectiveness has the better system using attitude.

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Parkinson's disease is a progressive disorder of the central nervous system which is marked by tremors, rigidity of muscles, slow slurred speech, and vocal cord disorder which starts early. In Parkinson's disease voice disorders affect approximately 90% of patients. Here, we aim to predict if a person has Parkinson's disease using voice recording dataset of patients by using data mining algorithm decision tree (CART). The voice of the patients are recorded and is converted into voice attributes like jitter, shimmer, frequency by using PRAAT script. The voice recordings are tested to predict if a person has Parkinson's disease and also to tell the condition of the disease.

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Web mining is a combination of data mining and World Wide Web. It consists of three types namely web structure mining, web content mining and web usage mining. Web Usage Mining is one of the parts of web mining and extracts the web users' behavior from web log file. This paper consists of three phases. The first one is data preprocessing phase, which is the most important one because it makes the data with good quality. This can be done by data cleaning, user identification, and session identification. The next one is pattern discovery phase; in this the users' navigational pattern and rules are extracted by using apriori algorithm. Final one is pattern analysis phase, which is used to analyze and visualize the rules. The aim of this paper is to identify the frequent link from web log data by using the Apriori algorithm.

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Due to the increasing use of internet, Ecommerce has become part and parcel of everyone's life. So there is a need to understand buying trends of people by the Sellers to improve business. One way to accomplish this is it to analyze the transactions to find out the items that are frequently bought together. In this paper, we propose a model where in multiple parties come together to analyze the buying trends by sharing their individual transactional databases, which we call it as multiparty collaborative data mining. But the privacy of sensitive data in multiparty collaborative mining is a major issue. The participants collaboratively conduct data mining to extract useful information from the database. In this approach, each participant has sensitive and non-sensitive data in their local database. Therefore, an important challenge of privacy preserving collaborative data mining (PPCDM) is how multiple parties efficiently conduct data mining without exposing each participant's sensitive information. This paper proposes the idea of preserving privacy of sensitive information by hiding sensitive information that are in the form of frequent itemsets. As a first step, we find the frequent itemsets using Apriori algorithm. The sensitive frequent itemsets are removed from the database using hiding algorithm and using border revision technique and Binary Integer Programming (BIP) technique we sanitize the database which we finally share with other participants for data mining. We use CPLEX solver to find solutions for hiding sensitive frequent itemsets, which provide solution to the investigated problem of disclosure of sensitive data.

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The aim of this paper is to examine possibilities for the initial data analyses of the failure data from industrial production process. To perform the initial data analysis of the data from production process we have used graphical statistical method and also data mining methods like drill-down analysis and cluster analysis. Before applying mentioned techniques and methods it was necessary to know the principle of the industrial production process itself and also to be aware of the failure data structure. This initial data analysis is vital to be able to review the knowledge potential of given data. Based on this, we are able to point out interesting issues, that can be further solved with KDD (knowledge discovery from databases) techniques.

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In this paper, we present a method to extract the possible relationships between knowledge points by analyzing e-book log and mining quiz data and mining Wikipedia articles. This method will be implemented in an ontology-based visualization support system to support the instructor to construct course-centered ontologies semi-automatically.

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Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance.

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