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Emily St. John Mandel: IMG Powell’s Q&A: Emily St. John Mandel



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    Station Eleven

    Emily St. John Mandel 9780385353304

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25 Remote Warehouse Computers Reference- General

This title in other editions

Other titles in the Springer Theses series:

Advances in K-Means Clustering: A Data Mining Thinking (Springer Theses)

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Advances in K-Means Clustering: A Data Mining Thinking (Springer Theses) Cover

 

Synopses & Reviews

Publisher Comments:

Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.

Synopsis:

The K-means algorithm is commonly used in data mining and business intelligence. This award-winning research pioneers its application to the intricacies of 'big data', detailing a theoretical framework for aggregating and validating clusters with K-means.

Table of Contents

Cluster Analysis and K-means Clustering: An Introduction.- The Uniform Effect of K-means Clustering.- Generalizing Distance Functions for Fuzzy c-Means Clustering.- Information-Theoretic K-means for Text Clustering.- Selecting External Validation Measures for K-means Clustering.- K-means Based Local Decomposition for Rare Class Analysis.- K-means Based Consensus Clustering.

Product Details

ISBN:
9783642298066
Author:
Wu, Junjie
Publisher:
Springer
Subject:
Cluster analysis
Subject:
Cluster Validity
Subject:
Consensus Clustering
Subject:
Information-Theoretic Clustering
Subject:
K-means
Subject:
Point-to-Centroid Distance
Subject:
Rare Class Analysis
Subject:
Uniform Effect
Subject:
Data Mining and Knowledge Discovery
Subject:
Statistics for Business/Economics/Mathematical Finance/Insurance
Subject:
BUSINESS INFORMATION SYSTEMS
Subject:
Database management
Subject:
Computers-Reference - General
Copyright:
Edition Description:
2012
Series:
Springer Theses
Publication Date:
20120630
Binding:
HARDCOVER
Language:
English
Pages:
194
Dimensions:
235 x 155 mm

Related Subjects

Business » General
Business » Management
Business » Writing
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Software Engineering » Software Management
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Advances in K-Means Clustering: A Data Mining Thinking (Springer Theses) New Hardcover
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Product details 194 pages Springer - English 9783642298066 Reviews:
"Synopsis" by , The K-means algorithm is commonly used in data mining and business intelligence. This award-winning research pioneers its application to the intricacies of 'big data', detailing a theoretical framework for aggregating and validating clusters with K-means.
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