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Principles of Data Mining

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Author:  David Hand, Heikki Mannila and Padhraic Smyth 
















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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