Titlebook: Unsupervised Classification; Similarity Measures, Sanghamitra Bandyopadhyay,Sriparna Saha Textbook 2013 Springer-Verlag Berlin Heidelberg 2

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Symmetry-Based Automatic Clustering,enetic algorithm-based automatic clustering techniques are described briefly. Thereafter a recently developed point symmetry-based automatic genetic clustering technique, VGAPS, is described in detail; it uses .-index for computing the fitness of the chromosomes. In VGAPS clustering, the assignment

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Some Line Symmetry Distance-Based Clustering Techniques,ng these distances are described. A moment-based approach is first used for defining the distance; it is applicable only for two-dimensional data sets. Analogous to GAPS, a genetic clustering technique with line symmetry distance (GALSD) is developed. The GALSD clustering technique can cluster data

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Use of Multiobjective Optimization for Data Clustering,se the multiobjective simulated annealing technique, AMOSA, as the underlying optimization strategy. The first one (MOPS) partitions the data into a pre-specified number of clusters, while the second one (VAMOSA) can automatically determine the number of clusters. These are multiobjective extensions

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Similarity Measures,This chapter primarily describes different types of similarity measures, and their relative advantages and disadvantages. Several such measures are defined, and their importance as well as applicability is discussed. Finally existing approaches for data normalization are mentioned in brief.

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Sanghamitra Bandyopadhyay,Sriparna SahaDescribes both well-established and metaheuristic clustering techniques.Offers a theoretical analysis of symmetry-based clustering techniques.Includes extensive real-world applications in remote-sensi

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