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Principles of Data Mining

Principles of Data Mining
Language:
Author: David Hand, Heikki Mannila and Padhraic Smyth
Url: ftp://gamma.sbin.org/pub/doc/books/Principles_of_Data_Mining.pdf
Format: Pdf
Year: 2001
Category: Data Mining
Pages: 322
Clicks: 101

Description
Contents: 1. Fundamentals: Chapters 1 through 4 focus on the fundamental aspects of data and data analysis: introduction to data mining ( chapter 1), measurement (chapter 2), summarizing and visualizing data (chapter 3), and uncertainty and inference ( chapter 4). 2. Data Mining Components: Chapters 5 through 8 focus on what we term the "components" of data mining algorithms: these are the building blocks that can be used to systematically create and analyze data mining algorithms. In chapter 5 we discuss this systematic approach to algorithm analysis, and argue that this " component wise" view can provide a useful systematic perspective on what is often a very confusing landscape of data analysis algorithms to the novice student of the topic. In this context, we then delve into broad discussions of each component: model repr esentations in chapter 6, score functions for fitting the models to data in chapter 7, and optimization and search techniques in chapter 8. (Discussion of data management is deferred until chapter 12 .) 3. Data Mining Tasks and Algorithms: Having discussed the fundamental components in the first 8 chapters of the text, the remainder of the chapters (from 9 through 14) are then devoted to specific data mining tasks and the algorithms used to address them. We organize the basic tasks into density estimation and clustering ( chapter 9), classification (chapter 10), regression (chapter 11), pattern discovery (chapter 13), and retrieval by content (chapter 14). In each of these chap ters we use the framework of the earlier chapters to provide a general context for the discussion of specific algorithms for each task. For example, for classification we ask: what models and representations are plausible and useful? what score functions s hould we, or can we, use to train a classifier? what optimization and search techniques are necessary? what is the computational complexity of each approach once we implement it as an actual algorithm? Our hope is that this general approach will provide thereader with a "roadmap" to an understanding that data mining algorithms are based on some very general and systematic principles, rather than simply aing blocks that can be used to systematically create and analyze data mining algorithms. In chapter 5 we discuss this systematic approach to algorithm analysis, and argue that this " component - wise" view can provide a useful systematic perspective on what is often a very confusing landscape of data analysis algorithms to the novice student of the topic. In this context, we then delve into broad discussions of each component: model repr esentations in chapter 6, score functions for fitting the models to data in chapter 7, and optimization and search techniques in chapter 8. cornucopia of seemingly unrelated and exotic algorithm.

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