Data Clustering

<p>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 <strong>Data Clustering: Algorithms and Applications</strong> 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.<br><br>The book focuses on three primary aspects of data clustering:</p><ul> <li> <em>Methods</em> 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</li> <li> <em>Domains</em> 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 </li> <li> <em>Variations and Insights</em> discussing important variations of the clustering process such as semisupervised clustering interactive clustering multiview clustering cluster ensembles and cluster validation</li> </ul><p>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.</p>