Clustering Heterogeneous Data with k-Means by Mutual Information-Based Unsupervised Feature Transformation

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...

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Bibliographic Details
Main Authors: Min Wei, Tommy W. S. Chow, Rosa H. M. Chan
Format: Article
Language:English
Published: MDPI AG 2015-03-01
Series:Entropy
Subjects:
feature transformation
k-means
clustering heterogeneous data
numerical features
non-numerical features
Online Access:http://www.mdpi.com/1099-4300/17/3/1535

Internet

http://www.mdpi.com/1099-4300/17/3/1535

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