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Data Mining Via Entropy and Graph Clustering

In: Data Mining in Biomedicine

Author

Listed:
  • Anthony Okafor

    (University of Florida)

  • Panos Pardalos

    (University of Florida)

  • Michelle Ragle

    (University of Florida)

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.

Suggested Citation

  • Anthony Okafor & Panos Pardalos & Michelle Ragle, 2007. "Data Mining Via Entropy and Graph Clustering," Springer Optimization and Its Applications, in: Panos M. Pardalos & Vladimir L. Boginski & Alkis Vazacopoulos (ed.), Data Mining in Biomedicine, pages 117-131, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-69319-4_7
    DOI: 10.1007/978-0-387-69319-4_7
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