Joydeep Ghosh

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. Expand

This paper presents the top 10 data mining algorithms identified by the International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Expand

In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences. Expand

We formally define the cluster ensemble problem as an optimization problem and propose three effective and efficient combiners for solving it based on a hypergraph model. Expand

This paper proposes 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. Expand

In this paper, we focus on data selection and classifier training methods, in order to 'prepare' classifiers for combining, especially when the training data are in limited supply. Expand

We present 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. Expand

This work investigates two approaches based on the concept of random forests of classifiers implemented within a binary hierarchical multiclassifier, with the goal of achieving improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited. Expand