Clustering Heterogeneous Data with k-Means by Mutual Information-Based Unsupervised Feature Transformation
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 be...
Main Authors: | Min Wei, Tommy W. S. Chow, Rosa H. M. Chan |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2015-03-01
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Series: | Entropy |
Subjects: | |
Online Access: | http://www.mdpi.com/1099-4300/17/3/1535 |
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