Knowledge Discovery in Databases: Cluster Analysis, Multifactor Dimensionality Reduction, Jumper 2.0, Alternating Decision Tree, Knowledge Tags

Chapters: Cluster Analysis, Multifactor Dimensionality Reduction, Jumper 2.0, Alternating Decision Tree, Knowledge Tags, Sonification, Optimal Matching, Unstructured Data, Information Fuzzy Networks, Id3 Algorithm, Health Research Development Information Network, Silhouette, Knowledge Discovery, Cluster-Weighted Modeling. Source: Wikipedia. Pages: 90. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology and typological analysis. Hierarchical algorithms find successive clusters using previously established clusters. These algorithms usually are either agglomerative ("bottom-up") or divisive ("top-down"). Agglomerative algorithms begin with each element as a separate cluster and merge them into successively larger clusters. Divisive algorithms begin with the whole set and proceed to divide it into successively smaller clusters. Partitional algorithms typically determine all clusters at once, but can also be used as divisive algorithms in the hierarchical clustering. Density-based clustering algorithms are devised to discover arbitrary-shaped clusters. In this approach, a cluster is regarded as a region in which the density of data objects exceeds a threshold. DBSCAN and OPTICS are two typical algorithms of this kind. Subspace clustering methods look for clusters that...More: http://booksllc.net/?id=669675