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, 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

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
Chapter 12: Clustering Categorical Data
Chapter 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
Chapter 24: Educational and Software Resources for DataClustering