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Mathematical Problems in Engineering
Volume 2015, Article ID 535932, 9 pages
Research Article

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

1Department of Automation, Tsinghua University, Beijing 100084, China
2Army Aviation Institute, Beijing 101123, China

Received 15 January 2015; Accepted 2 March 2015

Academic Editor: Hui Zhang

Copyright © 2015 Cheng Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.