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Principles of Data Mining (Adaptive Computation and Machine Learning)

by

Principles of Data Mining (Adaptive Computation and Machine Learning) Cover

 

Synopses & Reviews

Publisher Comments:

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Synopsis:

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

Synopsis:

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

Synopsis:

The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigour. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural network, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data and data preprocessing.

Description:

Includes bibliographical references (p. [491]-524) and index.

Product Details

ISBN:
9780262082907
Author:
Hand, David J.
Author:
Mannila, Heikki
Author:
Hand, D. J.
Author:
Smyth, Padhraic
Publisher:
A Bradford Book
Location:
Cambridge, Mass.
Subject:
General
Subject:
Information Management
Subject:
Management Information Systems
Subject:
Database Management - Data Warehousing and Data Mining
Subject:
Data mining
Subject:
Database Management - Database Mining
Subject:
Artificial Intelligence - General
Subject:
Artificial Intelligence
Subject:
Intelligence (AI) & Semantics
Subject:
Computers-Reference - General
Series:
Adaptive Computation and Machine Learning series Principles of Data Mining
Series Volume:
book 2
Publication Date:
20010817
Binding:
Hardback
Grade Level:
Professional and scholarly
Language:
English
Pages:
584
Dimensions:
9 x 6 in
Age Level:
from 18

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Related Subjects

Business » Accounting and Finance
Computers and Internet » Artificial Intelligence » General
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Product details 584 pages Bradford Book - English 9780262082907 Reviews:
"Synopsis" by , The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.
"Synopsis" by , The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.
"Synopsis" by , The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigour. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural network, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data and data preprocessing.
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