Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81840
Title: K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation
Authors: Wang, Yangtao
Chen, Lihui
Keywords: affinity propagation
clustering
Issue Date: 2014
Source: Wang, Y & Chen, L. (2014). K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation. 2014 IEEE International Conference on Data Mining (ICDM), 1091-1096.
Abstract: Recently, an attractive clustering approach named multi-exemplar affinity propagation (MEAP) has been proposed as an extension to the single exemplar based Affinity Propagation( AP). MEAP is able to automatically identify multiple exemplars for each cluster associated with a superexemplar. However, if the cluster number is a prior knowledge and can be specified by the user, MEAP is unable to make use of such knowledge directly in its learning process. Instead it has to rely on re-running the process as many times as it takes by tuning parameters until it generates the desired number of clusters. The process of MEAP re-running may be very time consuming. In this paper, we propose a new clustering algorithm called KMEAP which is able to generate specified K clusters directly while retaining the advantages of MEAP. Two kinds of new additional messages are introduced in MEAP in order to control the number of clusters in the process of message passing. The detailed problem formulation, the derived updating rules for passing messages, and the in-depth analysis of the proposed K-MEAP are provided. Experimental studies demonstrated that K-MEAP not only generates K clusters directly and efficiently without tuning parameters, but also outperforms related approaches in terms of clustering accuracy.
URI: https://hdl.handle.net/10356/81840
http://hdl.handle.net/10220/39690
ISSN: 1550-4786
DOI: 10.1109/ICDM.2014.54
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICDM.2014.54].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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