| M. Wallace, Ph. Mylonas and S. Kollias |
| Detecting and Verifying Dissimilar Patterns in Unlabelled Data |
| Advances in Soft Computing, Soft Computing: Methodologies and Applications, Springer Berlin/Heidelberg, pp. 247 - 258, 2005 |
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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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| 23 December , 2005 |
| M. Wallace, Ph. Mylonas and S. Kollias, "Detecting and Verifying Dissimilar Patterns in Unlabelled Data", Advances in Soft Computing, Soft Computing: Methodologies and Applications, Springer Berlin/Heidelberg, pp. 247 - 258, 2005 |
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