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-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

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  • Cheng Lu
  • Shiji Song
  • Cheng Wu

Abstract

The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K -nearest neighbor intervals (KNNI) for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

Suggested Citation

  • Cheng Lu & Shiji Song & Cheng Wu, 2015. "-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, September.
    • Handle: RePEc:hin:jnlmpe:535932
    DOI: 10.1155/2015/535932
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