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DEXAW
2009
IEEE
173views Database» more  DEXAW 2009»
14 years 4 months ago
Automatic Cluster Number Selection Using a Split and Merge K-Means Approach
Abstract—The k-means method is a simple and fast clustering technique that exhibits the problem of specifying the optimal number of clusters preliminarily. We address the problem...
Markus Muhr, Michael Granitzer
JMLR
2010
211views more  JMLR 2010»
13 years 4 months ago
Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering
In this paper, we introduce an assumption which makes it possible to extend the learning ability of discriminative model to unsupervised setting. We propose an informationtheoreti...
Bo Dai, Baogang Hu
ICDM
2009
IEEE
153views Data Mining» more  ICDM 2009»
13 years 7 months ago
A New Clustering Algorithm Based on Regions of Influence with Self-Detection of the Best Number of Clusters
Clustering methods usually require to know the best number of clusters, or another parameter, e.g. a threshold, which is not ever easy to provide. This paper proposes a new graph-b...
Fabrice Muhlenbach, Stéphane Lallich
AAAI
2010
13 years 6 months ago
A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra
Some real-world problems are partially decomposable, in that they can be decomposed into a set of coupled subproblems, that are each relatively easy to solve. However, when these ...
Siamak (Moshen) Ravanbakhsh, Barnabás P&oac...
JISE
2010
144views more  JISE 2010»
13 years 4 months ago
Variant Methods of Reduced Set Selection for Reduced Support Vector Machines
In dealing with large datasets the reduced support vector machine (RSVM) was proposed for the practical objective to overcome the computational difficulties as well as to reduce t...
Li-Jen Chien, Chien-Chung Chang, Yuh-Jye Lee