| M. Wallace, Ph. Mylonas, S. Kollias |
| Detecting and Verifying Dissimilar Patterns in Unlabelled Data |
| 8th Online World Conference on Soft Computing in Industrial Applications (WSC8), September - October 2003 |
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ABSTRACT
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| Clustering of unlabelled data is a difficult problem with numerous applications in various fields. When input space dimensions are many, the number of distinct patterns in the data is not known a priori, and feature scales are different, then the problem becomes much harder. In this paper we deal with such a problem. Our approach is based on an extension to hierarchical clustering that makes it suitable for data sets with numerous independent features. The results of this initial clustering are refined via a reclassification step. The issue of evaluation of hierarchical clustering methods is also discussed. The performance of the proposed methodology is demonstrated through the application to a synthetic data set and verified through application to a variety of well known machine learning data sets.
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| 14 October , 2003 |
| M. Wallace, Ph. Mylonas, S. Kollias, "Detecting and Verifying Dissimilar Patterns in Unlabelled Data", 8th Online World Conference on Soft Computing in Industrial Applications (WSC8), September - October 2003 |
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