S
Sanghamitra Bandyopadhyay
Researcher at Indian Statistical Institute
Publications - 376
Citations - 14754
Sanghamitra Bandyopadhyay is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Cluster analysis & Fuzzy clustering. The author has an hindex of 50, co-authored 360 publications receiving 13375 citations. Previous affiliations of Sanghamitra Bandyopadhyay include University of Maryland, Baltimore County & Tsinghua University.
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Genetic algorithm-based clustering technique
TL;DR: The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.
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Performance evaluation of some clustering algorithms and validity indices
TL;DR: This article evaluates the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, andA recently developed index I.
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A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
TL;DR: A simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration that is found to be significantly superior for many objective test problems.
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Validity index for crisp and fuzzy clusters
TL;DR: A cluster validity index and its fuzzification is described, which can provide a measure of goodness of clustering on different partitions of a data set, and results demonstrating the superiority of the PBM-index in appropriately determining the number of clusters are provided.
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Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients
TL;DR: A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front.