GFCM: A gossip-based fuzzy C-means algorithm

Author

Kalantarian, Z.S. ; Mashayekhi, H. ; Abdoshahi, A.

Author_Institution

Comput. Eng. Dept., Islamic Azad Univ., Shahrood, Iran

fYear

2014

fDate

27-29 May 2014

Firstpage

152

Lastpage

157

Abstract

In real-world networks, large amounts of data are distributed among nodes. Clustering is a useful method for analyzing large data sets and the Fuzzy C-Means (FCM) algorithm is one of the most popular methods for fuzzy clustering. In this paper, we propose GFCM, a gossip-based fuzzy C-means algorithm, for collective discovery of a clustering model from data residing at individual data sites. The proposed algorithm consists of local and collaborative phases. Nodes continuously collaborate with each other to maintain a summarized view of the data set, and to find the correct fuzzy clustering model for their local data. Gossiping is used as a robust method for communication between nodes. The experimental results show that GFCM can detect fuzzy clusters efficiently, with bounded communication costs. Its performance is also compared the recently proposed CFCM algorithm.

Keywords

data analysis; fuzzy set theory; pattern clustering; FCM algorithm; GFCM; bounded communication cost; clustering model collective discovery; data clustering; fuzzy cluster detection; fuzzy clustering model; gossip-based fuzzy C-means algorithm; gossiping; large data set analysis; node communication; node continuous collaboration; Analytical models; Erbium; Robustness; Vectors; collaborative clustering; distributed fuzzy clustering; fuzzy c-means;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Knowledge Technology (IKT), 2014 6th Conference on

Conference_Location

Shahrood

Print_ISBN

978-1-4799-5658-6

Type

conf

DOI

10.1109/IKT.2014.7030350

Filename

7030350