A Comparison of Cluster Analysis and Fuzzy ART

• Main Authors: Chen, Wan-Yu, 陳琬渝
• Other Authors: Lee-Ing Tong
• Format: Others
• Language: zh-TW
• Published: 1997
• Online Access: http://ndltd.ncl.edu.tw/handle/43723664998169191671

碩士 === 國立交通大學 === 工業工程與管理學系 === 85 === In many areas, cluster techniques are often used to classify the subjects that are similar to each other with respective to certain common characteristics. Cluster Analysis is a conventional multivariate statistical technique to form the homogeneous groups or clusters. Fuzzy Adaptive Resonance Theory( Fuzzy ART), one of the new artificial neural networks, is an alternative cluster technique. The objective of this thesis is to compare the Cluster Analysis and Fuzzy ART in solving the clustering problems. Three real-word cases are given in this study to compare the effectiveness of the Cluster Analysis and Fuzzy ART. The results indicate that Fuzzy ART is more efficient and accurate in classifying the subjects into groups.