Fuzzy C-mean clustering algorithms based on picard iteration and particle swarm optimization

ASIA unversity > 資訊學院 > 生物資訊與醫學工程學系 > 會議論文 > Item 310904400/8778

請使用永久網址來引用或連結此文件: http://asiair.asia.edu.tw/ir/handle/310904400/8778

題名:	Fuzzy C-mean clustering algorithms based on picard iteration and particle swarm optimization
作者:	Liu, Hsiang-Chuan;Yih, Jeng-Ming;Wu, Der-Bang;Liu, Shin-Wu
貢獻者:	Department of Bioinformatics
關鍵詞:	Copying;Fuzzy clustering;Fuzzy rules;Fuzzy systems;Geology;Particle swarm optimization (PSO);Remote sensing;Technical presentations;FCM algorithm;Fitness evaluations;Fuzzy C mean;Fuzzy c-mean clustering algorithm;Fuzzy C-means algorithms;Local minimums;Objective functions;Optimization problems;Picard iteration;Real data sets;Real-world application;Robust strategy
日期:	2009
上傳時間:	2010-04-08 12:06:01 (UTC+0)
出版者:	Asia University
摘要:	The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM)", is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm. © 2008 IEEE.
關聯:	2008 International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008 2:838-842
顯示於類別:	[生物資訊與醫學工程學系 ] 會議論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
		0Kb	Unknown	563	檢視/開啟
101.doc		31Kb	Microsoft Word	429	檢視/開啟

在ASIAIR中所有的資料項目都受到原著作權保護.

資料載入中.....