Apply Extended Self-Organizing Map to Analyze Mixed-Type Data

• Main Authors: Shu-Han Lin, 林書漢
• Other Authors: Chung-Chian Hsu
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/98921067198051589700

碩士 === 雲林科技大學 === 資訊管理系碩士班 === 98 === Mixed numeric and categorical data are commonly seen in nowadays corporate databases in which precious patterns may be hidden. Analyzing mixed-type data to extract the hidden patterns valuable to decision-making is therefore beneficial and critical for corporations to remain competitive. In addition, visualization facilitates exploration in the early stage of data analysis. In the paper, we present a visualized approach for analyzing multivariate mixed-type data. The proposed framework based on an extended self-organizing map allows visualized data cluster analysis as well as classification. We demonstrate the feasibility of the approach by analyzing two real-world datasets and compare our approach with other existing models to show its advantages.