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Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

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We are deluged by data—scientific data, medical data, demographic data, financial data, and marketing data. People have no time to look at this data. Human attention has become a precious resource. So, we must find ways to automatically analyze the data, to automatically classify it, to automatically summarize it, to automatically discover and characterize trends in it, and to automatically flag anomalies. This is one of the most active and exciting areas of the database research community. Researchers in areas such as statistics, visualization, artificial intelligence, and machine learning are contributing to this field. The breadth of the field makes it difficult to grasp its extraordinary progress over the last few years.

Jiawei Han and Micheline Kamber have done a wonderful job of organizing and presenting data mining in this very readable textbook. They begin by giving quick introductions to database and data mining concepts with particular emphasis on data analysis. They review the current product offerings by presenting a general framework that covers them all. They then cover in a chapter-by-chapter tour the concepts and techniques that underlie classification, prediction, association, and clustering. These topics are presented with examples, a tour of the best algorithms for each problem class, and pragmatic rules of thumb about when to apply each technique. I found this presentation style to be very readable, and J certainly learned a lot from reading the book, jiawei Han and Micheline Kamber have been leading contributors to data mining research. This is the text they use with their students to bring them up to speed on the field. The field is evolving very rapidly, but this book is a quick way to learn the basic ideas, and to understand where the field is today. I found it very informative and stimulating, and I expect you will too.

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