Data Mining Via Entropy and Graph Clustering

Anthony Okafor, Panos Pardalos and Michelle Ragle
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Anthony Okafor: University of Florida
Panos Pardalos: University of Florida
Michelle Ragle: University of Florida

A chapter in Data Mining in Biomedicine, 2007, pp 117-131 from Springer

Abstract: Abstract Data analysis often requires the unsupervised partitioning of the data set into clusters. Clustering data is an important but a difficult problem. In the absence of prior knowledge about the shape of the clusters, similarity measures for a clustering technique are hard to specify. In this work, we propose a framework that learns from the structure of the data. Learning is accomplished by applying the K-means algorithm multiple times with varying initial centers on the data via entropy minimization. The result is an expected number of clusters and a new similarity measure matrix that gives the proportion of occurrence between each pair of patterns. Using the expected number of clusters, final clustering of data is obtained by clustering a sparse graph of this matrix.

Keywords: K-means clustering; Entropy; Bayesian inference; Maximum spanning tree; Graph Clustering (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-0-387-69319-4_7

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