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.

chapter 1|28 pages

An Introduction to Cluster Analysis

ByCharu C. Aggarwal

chapter 2|32 pages

Feature Selection for Clustering: A Review

BySalem Alelyani, Jiliang Tang, Huan Liu

chapter 3|26 pages

Probabilistic Models for Clustering

ByHongbo Deng, Jiawei Han

chapter 4|24 pages

A Survey of Partitional and Hierarchical Clustering Algorithms

ByChandan K. Reddy, Bhanukiran Vinzamuri

chapter 5|17 pages

Density-Based Clustering

ByMartin Ester

chapter 6|21 pages

Grid-Based Clustering

ByWei Cheng, Wei Wang, Sandra Batista

chapter 7|28 pages

Nonnegative Matrix Factorizations for Clustering: A Survey

ByTao Li, Cha-charis Ding

chapter 8|24 pages

Spectral Clustering

ByJialu Liu, Jiawei Han

chapter 9|30 pages

Clustering High-Dimensional Data

ByArthur Zimek

chapter 10|28 pages

A Survey of Stream Clustering Algorithms

ByCharu C. Aggarwal

chapter 11|18 pages

Big Data Clustering

ByTong Hanghang, U. Kang

chapter 12|28 pages

Clustering Categorical Data

ByBill Andreopoulos

chapter 13|34 pages

Document Clustering: The Next Frontier

ByDavid C. Anastasiu, Andrea Tagarelli, George Karypis

chapter 14|18 pages

Clustering Multimedia Data

ByShen-Fu Tsai, Guo-Jun Qi, Shiyu Chang, Min-Hsuan Tsai, Thomas S. Huang

chapter 15|24 pages

Time-Series Data Clustering

ByDimitrios Kotsakos, Goce Trajcevski, Dimitrios Gunopulos, Charu C. Aggarwal

chapter 16|34 pages

Clustering Biological Data

ByChandan K. Reddy, Mohammad Al Hasan, Mohammed J. Zaki

chapter 17|42 pages

Network Clustering

BySrinivasan Parthasarathy, S. M. Faisal

chapter 18|26 pages

A Survey of Uncertain Data Clustering Algorithms

ByCharu C. Aggarwal

chapter 19|22 pages

Concepts of Visual and Interactive Clustering

ByAlexander Hinneburg

chapter 20|30 pages

Semisupervised Clustering

ByAmrudin Agovic, Arindam Banerjee

chapter 21|16 pages

Alternative Clustering Analysis: A Review

ByJames Bailey

chapter 22|20 pages

Cluster Ensembles: Theory and Applications

ByJoydeep Ghosh, Ayan Acharya

chapter 23|36 pages

Clustering Validation Measures

ByHui Xiong, Zhongmou Li

chapter 24|10 pages

Educational and Software Resources for Data Clustering

ByCharu C. Aggarwal, Chandan K. Reddy