Bibliographic Details
| Title: |
Apply extended self-organizing map to cluster and classify mixed-type data |
| Authors: |
Hsu, Chung-Chian hsucc@yuntech.edu.tw, Lin, Shu-Han1 g9723704@yuntech.edu.tw, Tai, Wei-Shen1 g9423803@yuntech.edu.tw |
| Source: |
Neurocomputing. Nov2011, Vol. 74 Issue 18, p3832-3842. 11p. |
| Subjects: |
Self-organizing maps, Data analysis, Mathematical category theory, Databases, Cluster analysis (Statistics), Multivariate analysis |
| Abstract: |
Abstract: Mixed numeric and categorical data are commonly seen nowadays in 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 to 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 with other existing models to show its advantages. [Copyright &y& Elsevier] |
|
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| Database: |
Engineering Source |
