Apply Extended Self-Organizing Map to Analyze Mixed-Type Data
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 corporati...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/98921067198051589700 |
| Summary: | 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.
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