Affinity Propagation Clustering With Pairwise Constraints

DOI

10.2991/wartia-16.2016.106How to use a DOI?

Keywords

Affinity propagation, complex dataset, semi-supervised method, pairwise constraints.

Abstract

With the explosive growing of data, there are challenges to deal with the large scale complex data. Many clustering algorithms have been proposed. Such as Affinity Propagation (AP) clustering Algorithm, AP takes similarity between pairs of data point as input measures. AP is a fast and efficient clustering algorithm for large dataset compared with the existing clustering algorithm. But for the datasets with complicated cluster structure, it cannot produce good clustering results. It can improve the clustering effect of AP by using the pairwise constraints and extended pairwise constraints to adjust the similarity matrix. Therefore, a semi-supervised method of affinity propagation clustering with pairwise constraints (AP with PC) is proposed in this paper. Experiments show that the method has good clustering result for complex datasets, moreover, the method is better than the comparative algorithm when the number of constraints for is large.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lijia Zhang
AU  - Lianglun Cheng
PY  - 2016/05
DA  - 2016/05
TI  - Affinity Propagation Clustering With Pairwise Constraints
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 525
EP  - 529
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.106
DO  - 10.2991/wartia-16.2016.106
ID  - Zhang2016/05
ER  -