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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Journal ArticleDOI
Topo2Vec: A Novel Node Embedding Generation Based on Network Topology for Link Prediction
TL;DR: A naive and scalable approach for generating the node samples based on the principle of goal-oriented greedy searching has been proposed, which can represent the relation of edges of the network in a better way, compared to the state-of-the-art methods.
Journal ArticleDOI
On principle axis based line symmetry clustering techniques
TL;DR: In this paper, a new line symmetry (LS) based distance is proposed which calculates the amount of symmetry of a point with respect to the first principal axis of a data set, and an evolutionary clustering technique is described that uses this new principal axis based LS distance for assignment of points to different clusters.
Proceedings ArticleDOI
Analysis of microarray data using multiobjective variable string length genetic fuzzy clustering
TL;DR: A novel multiobjective variable string length real coded genetic fuzzy clustering scheme for clustering microarray gene expression data has been proposed and Superiority of the proposed method over some other well known clustering algorithms has been demonstrated quantitatively.
Proceedings ArticleDOI
Differential explanations for energy management in buildings
TL;DR: In this article, the authors focus on building energy efficiency and how to enhance it by putting occupants in the loop of efficient energy use, supporting them to achieve their objectives by pointing out how far their actions are from an optimal set of actions.
Journal ArticleDOI
CODC: A copula based model to identify differential coexpression
TL;DR: This work exploits a copula-based framework to model differential coexpression between gene pairs in two different conditions and identifies differentially coexpressed modules by applying hierarchical clustering on the distance matrix.