Data Clustering : Algorithms and Applications

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Başlık:

Yazar:

Aggarwal, Charu C., editor.

ISBN:

9781315373515

Basım Bilgisi:

First edition.

Fiziksel Tanımlama:

1 online resource (652 pages) : 138 illustrations, text file, PDF.

Seri:

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series.

İçerik:

An Introduction to Cluster Analysis Charu C. Aggarwal--Feature Selection for Clustering: A Review Salem Alelyani, Jiliang Tang, and Huan Liu--Probabilistic Models for Clustering Hongbo Deng and Jiawei Han--A Survey of Partitional and Hierarchical Clustering Algorithms Chandan K. Reddy and Bhanukiran Vinzamuri--Density-Based Clustering Martin Ester--Grid-Based Clustering Wei Cheng, Wei Wang, and Sandra Batista--Non-Negative Matrix Factorizations for Clustering: A Survey Tao Li and Chris Ding--Spectral Clustering Jialu Liu and Jiawei Han--Clustering High-Dimensional Data Arthur Zimek--A Survey of Stream Clustering Algorithms Charu C. Aggarwal--Big Data Clustering Hanghang Tong and U. Kang--Clustering Categorical Data Bill Andreopoulos--Document Clustering: The Next Frontier David C. Anastasiu, Andrea Tagarelli, and George Karypis--Clustering Multimedia Data Shen-Fu Tsai, Guo-Jun Qi, Shiyu Chang, Min-Hsuan Tsai, and Thomas S. Huang--Time Series Data Clustering Dimitrios Kotsakos, Goce Trajcevski, Dimitrios Gunopulos, and Charu C. Aggarwal--Clustering Biological Data Chandan K. Reddy, Mohammad Al Hasan, and Mohammed J. Zaki--Network Clustering Srinivasan Parthasarathy and S.M. Faisal--A Survey of Uncertain Data Clustering Algorithms Charu C. Aggarwal--Concepts of Visual and Interactive Clustering Alexander Hinneburg--Semi-Supervised Clustering Amrudin Agovic and Arindam Banerjee--Alternative Clustering Analysis: A Review James Bailey--Cluster Ensembles: Theory and Applications Joydeep Ghosh and Ayan Acharya--Clustering Validation Measures Hui Xiong and Zhongmou Li--Educational and Software Resources for Data Clustering Charu C. Aggarwal and Chandan K. Reddy--Index.

Özet:

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.

Konu Terimleri:

File organization (Computer science)

COMPUTERS / Database Management / Data Mining.

COMPUTERS / Machine Theory.

characteristics of clustering problems.

grid-based clustering.

high-dimensional clustering.

methods for data clustering.