Data Clustering

Data Clustering: Algorithms and Applications

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Features

  • Presents core methods for data clustering, including probabilistic, density- and grid-based, and spectral clustering
  • Explores various problems and scenarios pertaining to multimedia, text, biological, categorical, network, streams, and uncertain data
  • Offers in-depth insight on the clustering process, including different ways to cluster the same data set
  • Includes an extensive bibliography at the end of each chapter

Summary

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

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

Author Bio(s)

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