Data Stream Mining Using Ensemble Classifier

A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many researchers. A rising problem in Data Streams is the handling of concept drift. To be a good algorithm it should adapt the changes and handle the concept drift properly. Ensemble classification method is the group of classifiers which works in collaborative manner. Overall this chapter will cover all the aspects of the data stream classification. The mission of this chapter is to discuss various techniques which use collaborative filtering for the data stream mining. The main concern of this chapter is to make reader familiar with the data stream domain and data stream mining. Instead of single classifier the group of classifiers is used to enhance the accuracy of classification. The collaborative filtering will play important role here how the different classifiers work collaborative within the ensemble to achieve a goal.

Visual Data Mining for Collaborative Filtering

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Association Rule Mining in Collaborative Filtering

Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering.

A Classification Framework Towards Application of Data Mining in Collaborative Filtering

Recommendation making is an important part of the information and e-commerce ecosystem. Recommendation represent a powerful method that filter large amount of information to provide relevant choice to end users. To provide recommendations to the users, efficient and cost effective methods needs to be introduced. Collaborative filtering is an emerging technique used in making recommendations which makes use of filtering by data mining. This chapter presents a classification framework on the use of data mining techniques in collaborative filtering to extract the best recommendations to the users on the basis of their interests.

Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value

Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five experiments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets.

Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System

This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what' can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what' can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning.

Combining User Co-Ratings and Social Trust for Collaborative Recommendation

Traditional collaborative filtering recommendation methods calculate similarity between users to find the most similar neighbors for a particular user and take into account their opinions to predict item ratings. Though these methods have some advantages, however, they encounter difficulties in dealing with the problems of cold start users and data sparsity. To overcome these difficulties, researchers have proposed to consider social context information in the process of determining similar neighbors. In this chapter, we present a data analytics approach that combines user preference and social trust for making better collaborative recommendation. The proposed approach regards the collaborative recommendation as a classification task. It includes a data analysis procedure to explore the target dataset in terms of user similarity and trust relationship, and a data classification procedure to extract data features and build up a model accordingly. A series of experiments are conducted for performance evaluation. The results show that this approach can be used to enhance the recommendation performance in an adaptive way for different datasets without an iterative parameter-tuning process.

Big Data Mining Using Collaborative Filtering

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering.

Collaborative Filtering Based Data Mining for Large Data

Size of data is increasing, it is creating challenges for its processing and storage. There are cluster based techniques available for storage and processing of this huge amount of data. Map Reduce provides an effective programming framework for developing distributed program for performing tasks which results in terms of key value pair. Collaborative filtering is the process of performing recommendation based on the previous rating of the user for a particular item or service. There are challenges while implementing collaborative filtering techniques using these distributed models. Some techniques are available for implementing collaborative filtering techniques using these models. Cluster based collaborative filtering, map reduce based collaborative filtering are some of these techniques. Chapter addresses these techniques and some basics of collaborative filtering.

History and Overview of the Recommender Systems

This Chapter analyzes the recommender systems, their history and its framework in brief. The current generation of filtering techniques in recommendation methods can be broadly classified into the following five categories. Techniques used in these categories are discussed in detail. Data mining algorithms techniques are implemented in recommender systems to filters user data ratings. Area of application of Recommender Systems gives broad idea and such as how it gives impact and why it is used in the e-commerce, Online Social Networks (OSN), and so on. It has shifted the core of Internet applications from devices to users. In this chapter, issues and recent research in recommender system are also discussed.