| Summary: | 碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 95 === With the development of information technology (IT), how to find useful information existed in vast data has become an important issue. The most broadly discussed technique is data mining, which has been successfully applied to many fields as analytic tool. Data mining extracts implicit, previously unknown, and potentially useful information from data. Clustering analysis is one of the most important and useful technologies in data mining methods. Clustering analysis is to group objects together, which is based on the difference of similarity on each object, and making highly homogeneity in the same cluster, or highly heterogeneity between each group.
Market segmentation is among the important task of survey industry. Market segmentation relies on the data clustering in a huge data set. Most survey companies apply analysis tools using conventional statistical analysis method with poor performance. In this study, we propose a market segmentation system based on the structure of decision support system which integrates conventional statistic analysis method and intelligent clustering methods such as artificial neural network, and particle swarm optimization methods. The proposed system is expected to provide marketing survey industry precise market segmentation for marketing strategy decision making and extended application.
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