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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Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Journal ArticleDOI
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
Alexander Strehl,Joydeep Ghosh +1 more
TL;DR: This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings and proposes three effective and efficient techniques for obtaining high-quality combiners (consensus functions).
Proceedings ArticleDOI
Clustering with Bregman Divergences
TL;DR: This paper proposes and analyzes parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences, and shows that there is a bijection between regular exponential families and a largeclass of BRegman diverGences, that is called regular Breg man divergence.
Journal ArticleDOI
Investigation of the random forest framework for classification of hyperspectral data
TL;DR: This work investigates two approaches based on the concept of random forests of classifiers implemented within a binary hierarchical multiclassifier system, with the goal of achieving improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited.
Journal ArticleDOI
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
TL;DR: A generative mixture-model approach to clustering directional data based on the von Mises-Fisher distribution, which arises naturally for data distributed on the unit hypersphere, and derives and analyzes two variants of the Expectation Maximization framework for estimating the mean and concentration parameters of this mixture.