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.
Papers
More filters
Journal ArticleDOI
Gene ordering in partitive clustering using microarray expressions.
TL;DR: The approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization.
Occupancy estimation using non intrusive sensors in energy efficient buildings
TL;DR: A general approach is proposed to estimate the number of occupants in a zone using different kinds of measurements such as motion detection, power consumption or CO2 concentration using a C4.5 learning algorithm that yields human readable decision trees.
Journal ArticleDOI
A multiobjective approach for identifying protein complexes and studying their association in multiple disorders
TL;DR: The task of identifying protein complexes as a multiobjective optimization problem is presented and identified protein complexes are found to be associated with several disorders classes like ‘Cancer’, ‘Endocrine’ and ‘Multiple’.
Book ChapterDOI
An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation
TL;DR: A multiobjective technique using improved differential evolution for fuzzy clustering has been proposed that optimizes multiple validity measures simultaneously and contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements.
Proceedings ArticleDOI
Multiobjective fuzzy biclustering in microarray data: Method and a new performance measure
TL;DR: An attempt has been made to develop a multiobjective genetic algorithm based approach for probabilistic fuzzy biclustering that minimizes the residual and maximizes cluster size and expression profile variance.