Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
- A. Strehl, Joydeep Ghosh
- Computer ScienceJournal of machine learning research
- 1 March 2003
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).
Top 10 algorithms in data mining
- Xindong Wu, Vipin Kumar, D. Steinberg
- Computer ScienceKnowledge and Information Systems
- 19 December 2007
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,…
Clustering with Bregman Divergences
- A. Banerjee, S. Merugu, I. Dhillon, Joydeep Ghosh
- Computer ScienceJournal of machine learning research
- 1 December 2005
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.
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
- A. Banerjee, I. Dhillon, Joydeep Ghosh, S. Sra
- Computer ScienceJournal of machine learning research
- 1 December 2005
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.
Cluster ensembles: a knowledge reuse framework for combining partitionings
- A. Strehl, Joydeep Ghosh
- Computer ScienceAAAI/IAAI
- 28 July 2002
This contribution is to formally define the cluster ensemble problem as an optimization problem and to propose three effective and efficient combiners for solving it based on a hypergraph model.
Discovering important people and objects for egocentric video summarization
- Yong Jae Lee, Joydeep Ghosh, K. Grauman
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 16 June 2012
This work introduced novel egocentric features to train a regressor that predicts important regions and produces significantly more informative summaries than traditional methods that often include irrelevant or redundant information.
Impact of Similarity Measures on Web-page Clustering
- A. Strehl, Joydeep Ghosh, R. Mooney
- Computer Science
- 2000
Comparing four popular similarity measures in conjunction with several clustering techniques, cosine and extended Jaccard similarities emerge as the best measures to capture human categorization behavior, while Euclidean performs poorest.
Data Clustering Algorithms And Applications
- Joydeep Ghosh, A. Acharya
- Computer Science
- 2013
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
- A. Banerjee, I. Dhillon, Joydeep Ghosh, S. Merugu, D. Modha
- Computer ScienceJournal of machine learning research
- 22 August 2004
This paper presents a substantially generalized co-clustering framework wherein any Bregman divergence can be used in the objective function, and various conditional expectation based constraints can be considered based on the statistics that need to be preserved.
Error Correlation and Error Reduction in Ensemble Classifiers
- Kagan Tumer, Joydeep Ghosh
- Computer ScienceConnection science
- 1 December 1996
This paper focuses on data selection and classifier training methods, in order to 'prepare' classifiers for combining, and discusses several methods that make the classifiers in an ensemble more complementary.
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