Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Interior Proximal and Multiplier Methods Based on Second Order Homogeneous Kernels
Mathematics of Operations Research
Concept decompositions for large sparse text data using clustering
Machine Learning
Convergence of Proximal-Like Algorithms
SIAM Journal on Optimization
Clustering large unstructured document sets
Computational information retrieval
Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A clustering scheme for large high-dimensional document datasets
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
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We present an optimization approach that generates k-means like clustering algorithms. The batch k-means and the incremental k-means are two well known versions of the classical k-means clustering algorithm (Duda et al. 2000). To benefit from the speed of the batch version and the accuracy of the incremental version we combine the two in a "ping--pong" fashion. We use a distance-like function that combines the squared Euclidean distance with relative entropy. In the extreme cases our algorithm recovers the classical k-means clustering algorithm and generalizes the Divisive Information Theoretic clustering algorithm recently reported independently by Berkhin and Becher (2002) and Dhillon1 et al. (2002). Results of numerical experiments that demonstrate the viability of our approach are reported.