Abstract
Since cluster analysis in data mining often deals with large-scale high-dimensional data with masking variables, it is important to remove non-contributing variables for accurate cluster recovery and also for proper interpretation of clustering results. Although the weights obtained by variable weighting methods can be used for the purpose of variable selection (or, elimination), they alone hardly provide a clear guide on selecting variables for subsequent analysis. In addition, variable selection and variable weighting are highly interrelated with the choice on the number of clusters. In this paper, we propose a non-parametric data clustering method, based on the W-k-means type clustering, for an automated and joint decision on selecting variables, determining variable weights, and deciding the number of clusters. Conclusions are drawn from computational experiments with random data and real-life data.
This work was supported by the Korea Research Foundation Grant (KRF-2003-041-D00629).
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Chung, JW., Choi, IC. (2006). A Non-parametric Method for Data Clustering with Optimal Variable Weighting. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_97
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DOI: https://doi.org/10.1007/11875581_97
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