k-nearest neighbor intervals based ap clustering algorithm for large incomplete data

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ID: 235154
2015
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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.
Reference Key
lu2015mathematicalk-nearest Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Cheng Lu;Shiji Song;Cheng Wu
Journal journal of power sources
Year 2015
DOI
10.1155/2015/535932
URL
http://dx.doi.org/10.1155/2015/535932
Keywords
civil engineering (general)mathematics engineering (general)

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