Data Clustering Algorithms and Applications Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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.

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.

Charu C. Aggarwal is a Research Scientist at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from Massachusetts Institute of Technology in 1996. His research interest during his Ph.D. years was in combinatorial optimization (network flow algorithms), and his thesis advisor was Professor James B. Orlin. He has since worked in the field of performance analysis, databases, and data mining. He has published over 200 papers in refereed conferences and journals, and has applied for or been granted over 80 patents. He is author or editor of nine books, including this one. Because of the commercial value of the above-mentioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of an IBM Research Division Award (2008) for his scientific contributions to data stream research. He has served on the program committees of most major database/data mining conferences, and served as program vice-chairs of the SIAM Conference on Data Mining, 2007, the IEEE ICDM Conference, 2007, the WWW Conference 2009, and the IEEE ICDM Conference, 2009. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering Journal from 2004 to 2008. He is an associate editor of the ACM TKDD Journal, an action editor of the Data Mining and Knowledge Discovery Journal, an associate editor of the ACM SIGKDD Explorations, and an associate editor of the Knowledge and Information Systems Journal. He is a fellow of the IEEE for "contributions to knowledge discovery and data mining techniques", and a life-member of the ACM.
Chandan K. Reddy is an Assistant Prof