Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar presents basic ideas and algorithms for those studying data mining for the primary time. Every main matter is organized into two chapters, beginning with basic ideas that provide needed background for understanding each knowledge mining method, adopted by extra superior ideas and algorithms.
The text requires only a modest background in mathematics. Numerous examples are offered to lucidly illustrate the key concepts. For my part that is at the moment the very best data mining text book on the market. I like the comprehensive coverage which spans all main information mining methods including classification, clustering, and sample mining (association guidelines).
Authors show how data mining can help the corporate by giving specific examples of how strategies, similar to clustering, classification, affiliation rule mining, and anomaly detection may be applied. You'll be able to describe the potential time complexity of anomaly detection approaches primarily based on model-based mostly using clustering, proximity-based mostly, and density. No data of specific strategies is required. Fairly, deal with the basic computational requirements of each approach, such because the time required to compute the density of each object.
This book includes extensive variety of built-in examples and figures, affords instructor assets including options for exercises and lecture slides. It assumes solely a modest statistics or arithmetic background, and no database data is needed. Matters covered include; predictive modeling, association analysis, clustering, anomaly detection, visualization.
More details about this book...
or
Download Introduction to Data Mining PDF Ebook :
0 comments:
Post a Comment