Applying HBMO and PSO in an Intelligent Market Segmentation System

• Main Authors: I-Ting Kuo, 郭宜婷
• Other Authors: 邱垂昱
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/3crdkj

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 96 === With the development of information technology, how to find useful information existed in vast data has become an important issue. The most broadly discusses technique is data mining, which has been successfully applied to many fields as analytic tool. 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 each industry. Market segmentation relies on the data clustering in a huge data set. Most 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 particle swarm optimization and honey bee mating optimization methods. The proposed system is expected to provide industry precise market segmentation for marketing strategy decision making and extended application.