K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation
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. Howe...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2016
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| Online Access: | https://hdl.handle.net/10356/81840 http://hdl.handle.net/10220/39690 |
| _version_ | 1826125294225850368 |
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| author | Wang, Yangtao Chen, Lihui |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wang, Yangtao Chen, Lihui |
| author_sort | Wang, Yangtao |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:34:17Z |
| format | Conference Paper |
| id | ntu-10356/81840 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:34:17Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/818402020-03-07T13:24:44Z K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation Wang, Yangtao Chen, Lihui School of Electrical and Electronic Engineering 2014 IEEE International Conference on Data Mining (ICDM) affinity propagation clustering 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. Accepted version 2016-01-13T05:54:48Z 2019-12-06T14:41:18Z 2016-01-13T05:54:48Z 2019-12-06T14:41:18Z 2014 Conference Paper 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. 1550-4786 https://hdl.handle.net/10356/81840 http://hdl.handle.net/10220/39690 10.1109/ICDM.2014.54 en © 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]. 6 p. application/pdf |
| spellingShingle | affinity propagation clustering Wang, Yangtao Chen, Lihui K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation |
| title | K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation |
| title_full | K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation |
| title_fullStr | K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation |
| title_full_unstemmed | K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation |
| title_short | K-MEAP: Generating Specified K Clusters with Multiple Exemplars by Efficient Affinity Propagation |
| title_sort | k meap generating specified k clusters with multiple exemplars by efficient affinity propagation |
| topic | affinity propagation clustering |
| url | https://hdl.handle.net/10356/81840 http://hdl.handle.net/10220/39690 |
| work_keys_str_mv | AT wangyangtao kmeapgeneratingspecifiedkclusterswithmultipleexemplarsbyefficientaffinitypropagation AT chenlihui kmeapgeneratingspecifiedkclusterswithmultipleexemplarsbyefficientaffinitypropagation |