Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering

碩士 === 國立金門技術學院 === 電資研究所 === 98 === The self-generation fuzzy density partitions algorithm is developed in this thesis. A particle swarm optimization (PSO) algorithm with the improvement of the fuzzy density measure is applied to generate correct clustering results in identifying their clusters for...

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Main Authors:	Hua-Ching Chen, 陳華慶
Other Authors:	Hsuan-Ming Feng
Format:	Others
Language:	zh-TW
Published:	2010
Online Access:	http://ndltd.ncl.edu.tw/handle/91523362416957788045

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spelling	ndltd-TW-098KMIT07060032015-11-09T04:05:39Z http://ndltd.ncl.edu.tw/handle/91523362416957788045 Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering 自我生成模糊密度分割演算法及其在資料分群之應用 Hua-Ching Chen 陳華慶碩士國立金門技術學院電資研究所 98 The self-generation fuzzy density partitions algorithm is developed in this thesis. A particle swarm optimization (PSO) algorithm with the improvement of the fuzzy density measure is applied to generate correct clustering results in identifying their clusters for different data sets. In this proposed learning method, the divided individual fuzzy partitions can represent the feature of the clustering data set. Five artificial data sets are considered as testing patterns to demonstrate the efficiency of the proposed method. Simulations compared with other traditional K-means and Fuzzy C-means clustering algorithms demonstrate the high performance of the proposed self-generation fuzzy density partitions algorithm. Hsuan-Ming Feng 馮玄明 2010 學位論文 ; thesis 103 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 國立金門技術學院 === 電資研究所 === 98 === The self-generation fuzzy density partitions algorithm is developed in this thesis. A particle swarm optimization (PSO) algorithm with the improvement of the fuzzy density measure is applied to generate correct clustering results in identifying their clusters for different data sets. In this proposed learning method, the divided individual fuzzy partitions can represent the feature of the clustering data set. Five artificial data sets are considered as testing patterns to demonstrate the efficiency of the proposed method. Simulations compared with other traditional K-means and Fuzzy C-means clustering algorithms demonstrate the high performance of the proposed self-generation fuzzy density partitions algorithm.
author2	Hsuan-Ming Feng
author_facet	Hsuan-Ming Feng Hua-Ching Chen 陳華慶
author	Hua-Ching Chen 陳華慶
spellingShingle	Hua-Ching Chen 陳華慶 Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering
author_sort	Hua-Ching Chen
title	Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering
title_short	Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering
title_full	Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering
title_fullStr	Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering
title_full_unstemmed	Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering
title_sort	self-generation fuzzy density partitions algorithm and it’s applications in data clustering
publishDate	2010
url	http://ndltd.ncl.edu.tw/handle/91523362416957788045
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Self-Generation Fuzzy Density Partitions Algorithm and It’s Applications in Data Clustering

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