Data mining and analysis : fundamental concepts and algorithms / Mohammed J. Zaki, Rensselaer Polytechnic Institute, Troy, New York, Wagner Meira, Jr., Universidade Federal de Minas Gerais, Brazil.

By:

Zaki, Mohammed J, 1971-

Contributor(s):

Meira, Wagner, 1967-

Material type: Text

TextPublisher: New York, NY : Cambridge University Press, 2014Description: xi, 593 pages : illustrations ; 27 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780521766333 (hardback : alk. paper)

Subject(s):

Data mining

DDC classification:

006.312 23 Z.M.D

LOC classification:

QA76.9.D343 Z36 2014

Online resources:

Abstract

Contents:

Data mining and analysis -- Numeric attributes -- Categorical attributes -- Graph data -- Kernel methods -- High-dimensional data -- Dimensionality reduction -- Itemset mining -- Summarizing itemsets -- Sequence mining -- Graph pattern mining -- Pattern and rule assessment -- Representative-based clustering -- Hierarchical clustering -- Density-based clustering -- Spectral and graph clustering -- Clustering validation -- Probabilistic classification -- Decision tree classifier -- Linear discriminant analysis -- Support vector machines -- Classification assessment.

Summary: A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	Main library A2	Computers & Information Technology ( Information systems )	006.312 Z.M.D (Browse shelf(Opens below))	Available		00012570

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Includes bibliographical references and index.

Data mining and analysis --
Numeric attributes --
Categorical attributes --
Graph data --
Kernel methods --
High-dimensional data --
Dimensionality reduction --
Itemset mining --
Summarizing itemsets --
Sequence mining --
Graph pattern mining --
Pattern and rule assessment --
Representative-based clustering --
Hierarchical clustering --
Density-based clustering --
Spectral and graph clustering --
Clustering validation --
Probabilistic classification --
Decision tree classifier --
Linear discriminant analysis --
Support vector machines --
Classification assessment.

A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

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