Association Rule Mining Using Vertical Apriori
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Association Rule Mining Using Vertical Apriori
The aim of data mining as a scientific research is developing methods to analyze large amounts of data in order to discover interesting regularities or exceptions. Typical problems, which should be resolved during developing effective data mining algorithms, arise from the large sizes of both: The data sets used in the data mining process and the patterns results sets (for example in rules) which form discovered knowledge. Scientific researchers are oriented to find the most advantageous (i.e. most effective) solutions both during the data preparation stage and exploration and finally post- processing to obtain results. During mining of association rules, the main effort has been put so far in developing more and more sophisticated mining algorithms finding interesting patterns in the appropriately prepared data. One problem that still needs to be tackled is the problem of excessive Database scans. Most of Association rules algorithms are extensions or derivatives of the Apriori algorithm, so mostly all of them use the technique of scanning the Database many times in order to obtain the association rules, this process (lot of Database Scans) is very time consuming. In this thesis we develop an optimization of the Apriori algorithm namely Vertical Apriori, using the C++ bitset data structure (an optimized version of bit vectors). Performance improvements will be demonstrated through our experiments section in chapter 6.
Frequent Pattern Mining
This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
Data Mining, Southeast Asia Edition
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site