Data Clustering Algorithms and Applications 1st Edition by Charu C Aggarwal,Chandan K Reddy ISBN 1466558229 9781466558229

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Product details:

ISBN 10: 1466558229

ISBN 13: 9781466558229 

Author: Charu C. Aggarwal, Chandan K. Reddy

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 feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization
• Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data
• Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation

In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Table of contents:

1. An Introduction to Cluster Analysis — Charu C. Aggarwal

2. Feature Selection for Clustering: A Review — Salem Alelyani, Jiliang Tang, and Huan Liu

3. Probabilistic Models for Clustering — Hongbo Deng and Jiawei Han

4. A Survey of Partitional and Hierarchical Clustering Algorithms — Chandan K. Reddy and Bhanukiran Vinzamuri

5. Density-Based Clustering — Martin Ester

6. Grid-Based Clustering — Wei Cheng, Wei Wang, and Sandra Batista

7. Non-Negative Matrix Factorizations for Clustering: A Survey — Tao Li and Chris Ding

8. Spectral Clustering — Jialu Liu and Jiawei Han

9. Clustering High-Dimensional Data — Arthur Zimek

10. A Survey of Stream Clustering Algorithms — Charu C. Aggarwal

11. Big Data Clustering — Hanghang Tong and U. Kang

12. Clustering Categorical Data — Bill Andreopoulos

13. Document Clustering: The Next Frontier — David C. Anastasiu, Andrea Tagarelli, and George Karypis

14. Clustering Multimedia Data — Shen-Fu Tsai, Guo-Jun Qi, Shiyu Chang, Min-Hsuan Tsai, and Thomas S. Huang

15. Time Series Data Clustering — Dimitrios Kotsakos, Goce Trajcevski, Dimitrios Gunopulos, and Charu C. Aggarwal

16. Clustering Biological Data — Chandan K. Reddy, Mohammad Al Hasan, and Mohammed J. Zaki

17. Network Clustering — Srinivasan Parthasarathy and S.M. Faisal

18. A Survey of Uncertain Data Clustering Algorithms — Charu C. Aggarwal

19. Concepts of Visual and Interactive Clustering — Alexander Hinneburg

20. Semi-Supervised Clustering — Amrudin Agovic and Arindam Banerjee

21. Alternative Clustering Analysis: A Review — James Bailey

22. Cluster Ensembles: Theory and Applications — Joydeep Ghosh and Ayan Acharya

23. Clustering Validation Measures — Hui Xiong and Zhongmou Li

24. Educational and Software Resources for Data Clustering — Charu C. Aggarwal and Chandan K. Reddy

25. Index

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Tags: Charu C Aggarwal, Chandan K Reddy, Data, Clustering, Algorithms, Applications