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Apply extended self-organizing map to cluster and classify mixed-type data

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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]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="AR" term="%22Hsu%2C+Chung-Chian%22">Hsu, Chung-Chian</searchLink><i> hsucc@yuntech.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Shu-Han%22">Lin, Shu-Han</searchLink><relatesTo>1</relatesTo><i> g9723704@yuntech.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Tai%2C+Wei-Shen%22">Tai, Wei-Shen</searchLink><relatesTo>1</relatesTo><i> g9423803@yuntech.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Neurocomputing%22">Neurocomputing</searchLink>. Nov2011, Vol. 74 Issue 18, p3832-3842. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+category+theory%22">Mathematical category theory</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink>
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  Label: Abstract
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  Data: 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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  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.neucom.2011.07.014
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      – Code: eng
        Text: English
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      – SubjectFull: Data analysis
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      – SubjectFull: Mathematical category theory
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      – SubjectFull: Databases
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      – SubjectFull: Cluster analysis (Statistics)
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      – SubjectFull: Multivariate analysis
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              Text: Nov2011
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              Y: 2011
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