Data Clustering : Algorithms and Applications.
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, fr...
Saved in:
| Online Access: |
Full Text (via ProQuest) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken :
CRC Press,
2013.
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| Series: | Chapman & Hall/CRC data mining and knowledge discovery series.
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| Subjects: |
MARC
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| 100 | 1 | |a Aggarwal, Charu C. | |
| 245 | 1 | 0 | |a Data Clustering : |b Algorithms and Applications. |
| 260 | |a Hoboken : |b CRC Press, |c 2013. | ||
| 300 | |a 1 online resource (648 pages) | ||
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| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Chapman & Hall/CRC Data Mining and Knowledge Discovery Series | |
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Front Cover; Contents; Preface; Editor Biographies; Contributors; Chapter 1: An Introduction to Cluster Analysis; Chapter 2: Feature Selection for Clustering: A Review; Chapter 3: Probabilistic Models for Clustering; Chapter 4: A Survey of Partitional and Hierarchical Clustering Algorithms; Chapter 5: Density-Based Clustering; Chapter 6: Grid-Based Clustering; Chapter 7: Nonnegative Matrix Factorizations for Clustering: A Survey; Chapter 8: Spectral Clustering; Chapter 9: Clustering High-Dimensional Data; Chapter 10: A Survey of Stream Clustering Algorithms; Chapter 11: Big Data Clustering. | |
| 505 | 8 | |a Chapter 12: Clustering Categorical DataChapter 13: Document Clustering: The Next Frontier; Chapter 14 : Clustering Multimedia Data; Chapter 15: Time-Series Data Clustering; Chapter 16: Clustering Biological Data; Chapter 17: Network Clustering; Chapter 18: A Survey of Uncertain Data Clustering Algorithms; Chapter 19: Concepts of Visual and Interactive Clustering; Chapter 20: Semisupervised Clustering; Chapter 21: Alternative Clustering Analysis: A Review; Chapter 22 : Cluster Ensembles: Theory and Applications; Chapter 23: Clustering ValidationMeasures. | |
| 505 | 8 | |a Chapter 24: Educational and Software Resources for DataClusteringColor Inserts; Back Cover. | |
| 520 | |a Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea. | ||
| 650 | 0 | |a Document clustering. | |
| 650 | 0 | |a Cluster analysis. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Machine theory. | |
| 650 | 0 | |a File organization (Computer science) | |
| 650 | 7 | |a Cluster analysis |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Document clustering |2 fast | |
| 650 | 7 | |a File organization (Computer science) |2 fast | |
| 650 | 7 | |a Machine theory |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Aggarwal, Charu C. |t Data Clustering : Algorithms and Applications. |d Hoboken : CRC Press, ©2013 |z 9781466558212 |
| 830 | 0 | |a Chapman & Hall/CRC data mining and knowledge discovery series. | |
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