Clustering Heterogeneous Data with k-Means by Mutual Information-Based Unsupervised Feature Transformation
| Autor: | Min Wei, Tommy W. S. Chow, Rosa H. M. Chan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Entropy, Vol 17, Iss 3, Pp 1535-1548 (2015) |
| Druh dokumentu: | article |
| ISSN: | 1099-4300 54661439 |
| DOI: | 10.3390/e17031535 |
| Popis: | Traditional centroid-based clustering algorithms for heterogeneous data with numerical and non-numerical features result in different levels of inaccurate clustering. This is because the Hamming distance used for dissimilarity measurement of non-numerical values does not provide optimal distances between different values, and problems arise from attempts to combine the Euclidean distance and Hamming distance. In this study, the mutual information (MI)-based unsupervised feature transformation (UFT), which can transform non-numerical features into numerical features without information loss, was utilized with the conventional k-means algorithm for heterogeneous data clustering. For the original non-numerical features, UFT can provide numerical values which preserve the structure of the original non-numerical features and have the property of continuous values at the same time. Experiments and analysis of real-world datasets showed that, the integrated UFT-k-means clustering algorithm outperformed others for heterogeneous data with both numerical and non-numerical features. |
| Databáze: | Directory of Open Access Journals |
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