Hardcover: 769 pages
Publisher: Pearson; 1 edition (May 12, 2005)
Product Dimensions: 7.4 x 1.6 x 9.4 inches
Shipping Weight: 3.1 pounds (View shipping rates and policies)
Average Customer Review: 3.9 out of 5 stars See all reviews (39 customer reviews)
Best Sellers Rank: #159,709 in Books (See Top 100 in Books) #39 in Books > Computers & Technology > Databases & Big Data > Data Warehousing #45 in Books > Computers & Technology > Networking & Cloud Computing > Network Administration > Storage & Retrieval #94 in Books > Computers & Technology > Databases & Big Data > Data Mining
Data mining could be considered to be "Artificial Intelligence Lite", since it deals with many of the same issues in learning, classification, and analysis as they occur in the field of artificial intelligence but does not have as its goal the construction of "thinking machines." Instead, the emphasis is on practical problems that are important in business and industry, even though the solutions of many of these problems makes use of techniques that a thinking machine should be expected to have. Data mining has become an enormous industry, and has even been the subject of political and legal concerns due to the efforts of some governments to mine data on its citizens. This book gives a general overview of data mining with emphasis on classification and associative analysis. Anyone who is interested in data mining could read the book, but some rather sophisticated background in mathematics will be needed to read some of the sections. Pseudocode is given throughout the book to illustrate the different data mining algorithms. There are also exercises at the end of each chapter, but noticeably missing in the book is the inclusion of real case studies in data mining. The inclusion of these case studies would alert the reader to the fact that data mining is of great interest from the standpoint of business and industry, and would lessen the belief that data mining is just another academic field or just another branch of statistics.Speaking somewhat loosely, the goal of data mining is to find interesting patterns in massive amounts of data or the classification of such patterns. This entails of course that one have a notion of what is "interesting" and one of the main problems in data mining is to find suitable `interestingness measures'.
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