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Full Description
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.
Contents
An Introduction to Cluster Analysis. Feature Selection for Clustering: A Review. Probabilistic Models for Clustering. A Survey of Partitional and Hierarchical Clustering Algorithms. Density-Based Clustering. Grid-Based Clustering. Non-Negative Matrix Factorizations for Clustering: A Survey. Spectral Clustering. Clustering High-Dimensional Data. A Survey of Stream Clustering Algorithms. Big Data Clustering. Clustering Categorical Data. Document Clustering: The Next Frontier. Clustering Multimedia Data. Time Series Data Clustering. Clustering Biological Data. Network Clustering. A Survey of Uncertain Data Clustering Algorithms. Concepts of Visual and Interactive Clustering. Semi-Supervised Clustering. Alternative Clustering Analysis: A Review. Cluster Ensembles: Theory and Applications. Clustering Validation Measures. Educational and Software Resources for Data Clustering. Index.
