Fuzzy Distance Measure Based Affinity Propagation Clustering

Al-Akash, Omar Mahmoud Nayef and Syed Ahmad, Sharifah Sakinah and Azmi, Mohd Sanusi (2018) Fuzzy Distance Measure Based Affinity Propagation Clustering. International Journal Of Applied Engineering Research, 13 (2). pp. 1501-1505. ISSN 0973-4562

Text non-indeks Omar Akash 91_62414-IJAER ok 1501-1505.pdf - Accepted Version Download (277kB)

Abstract

Affinity Propagation (AP) is an effective algorithm that find exemplars repeatedly exchange real valued messages between pairs of data points. AP uses the similarity between data points to calculate the messages. Hence, the construction of similarity is essential in the AP algorithm. A common choice for similarity is the negative Euclidean distance. However, due to the simplicity of Euclidean distance, it cannot capture the real structure of data. Furthermore, Euclidean distance is sensitive to noise and outliers such that the performance of the AP might be degraded. Therefore, researchers have intended to utilize different similarity measures to analyse the performance of AP. nonetheless, there is still a room to enhance the performance of AP clustering. A clustering method called fuzzy based Affinity propagation (F-AP) is proposed, which is based on a fuzzy similarity measure. Experiments shows the efficiency of the proposed F-AP, experiments is performed on UCI dataset. Results shows a promising improvement on AP.

Item Type:	Article
Uncontrolled Keywords:	Affinity propagation, fuzzy set, fuzzy similarity measure, clustering, unsupervised classification.
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Mohd Hannif Jamaludin
Date Deposited:	17 Jul 2019 04:12
Last Modified:	24 Aug 2021 13:56
URI:	http://eprints.utem.edu.my/id/eprint/22848
Statistic Details:	View Download Statistic

Actions (login required)

View Item