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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| Format: | Others |
| Language: | zh-TW |
| Published: |
2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/91523362416957788045 |
| Summary: | 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.
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