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Our world is driven by data, which must be analyzed and processed to convert it into information we can use. Data Mining, Second Edition, offers a comprehensive tour of the basic concepts, models, and methodologies developed to accomplish this task.
After introducing basic concepts of data mining, Mehmed Kantardzic reviews the characteristics of raw data sets and the techniques of data preprocessing. We also see how to transform raw data with missing values and time-dependent attributes.
Several chapters in the book give an overview of specific data-mining techniques. For example, statistical inference methods—such as Bayesian classifier, predictive and logistic regression, analysis of variance (ANOVA), and log-linear models—are reviewed. One chapter discusses the complexity of clustering problems and introduces agglomerative, partitional, and incremental clustering techniques. Different aspects of local modeling in large data sets are addressed, as are common techniques for association-rule mining. Web mining and text mining are becoming central topics for many researchers, and the associated algorithms are summarized here. Graph mining, as well as temporal and spatial mining, is covered as well. The final chapter recognizes the importance of data-mining visualization techniques for the representation of large-dimensional samples.
This second edition includes descriptions of recently developed techniques and methodologies, including support vector machines, Kohonen maps, the DBSCAN clustering algorithm, and parallel and distributed data mining.
Complete with chapter exercises, Data Mining, Second Edition, is an impressive overview of a critically important activity marking our Information Age.
Hardcover : 552 pages
Publisher: John Wiley & Sons, Inc. ( July 25, 2011 )
Item #: 13-441168
ISBN: 9780470890455
Product Dimensions: 6.125 x 9.25 inches
Product Weight: 32.0 ounces (View shipping rates and policies)

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