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Arindam Banerjee

Researcher at University of Minnesota

Publications -  269
Citations -  24362

Arindam Banerjee is an academic researcher from University of Minnesota. The author has contributed to research in topics: Cluster analysis & Estimator. The author has an hindex of 49, co-authored 253 publications receiving 20328 citations. Previous affiliations of Arindam Banerjee include University of Illinois at Urbana–Champaign & University of Texas at Austin.

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Journal ArticleDOI

Anomaly detection: A survey

Varun Chandola, +2 more
- 30 Jul 2009 - 
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Proceedings ArticleDOI

Clustering with Bregman Divergences

Arindam Banerjee, +3 more
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.
Proceedings Article

Semi-supervised Clustering by Seeding

Sugato Basu, +2 more
Journal ArticleDOI

TRY plant trait database : Enhanced coverage and open access

Jens Kattge, +754 more
- 01 Jan 2020 - 
TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements.
Journal ArticleDOI

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Arindam Banerjee, +3 more
- 01 Dec 2005 - 
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.