J
Joydeep Ghosh
Researcher at University of Texas at Austin
Publications - 513
Citations - 29870
Joydeep Ghosh is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Cluster analysis & Artificial neural network. The author has an hindex of 60, co-authored 474 publications receiving 26979 citations. Previous affiliations of Joydeep Ghosh include Los Angeles Mission College & National University of Singapore.
Papers
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Proceedings ArticleDOI
Privacy-preserving distributed clustering using generative models
Srujana Merugu,Joydeep Ghosh +1 more
TL;DR: A new measure that quantifies privacy based on information theoretic concepts is proposed, and it is shown that decreasing privacy leads to a higher quality of the combined model and vice versa, and high quality distributed clustering can be achieved with little privacy loss and low communication cost.
Journal ArticleDOI
Symbolic interpretation of artificial neural networks
Ismail Taha,Joydeep Ghosh +1 more
TL;DR: This paper presents three rule extraction techniques, one of which is specific to feedforward networks, with a single hidden layer of sigmoidal units, and a rule-evaluation technique, which orders extracted rules based on three performance measures.
Proceedings ArticleDOI
Model-based overlapping clustering
TL;DR: This paper interprets an overlapping clustering model proposed by Segal et al.
Dissertation
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Alexander Strehl,Joydeep Ghosh +1 more
TL;DR: This dissertation takes a relationship-based approach to cluster analysis of high (1000 and more) dimensional data that side-steps the ‘curse of dimensionality’ issue by working in a suitable similarity space instead of the original feature space, and proposes two frameworks that leverage graph algorithms to achieve relationship- based clustering and visualization, respectively.
Journal ArticleDOI
Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis
TL;DR: This paper introduces a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems, and introduces a generalised modular learning framework used to partition a set of classes into two disjoint groups called meta-classes.