Feature Weighting in k-Means Clustering.

Feature Weighting in k-Means Clustering.

Data sets with multiple, heterogeneous feature spaces occur frequently. We present an abstract framework for integrating multiple feature spaces in the k-means clustering algorithm. Our main ideas are (i) to represent each data object as a tuple of multiple feature vectors, (ii) to assign a suitable...

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Pubblicato in:Machine learning. 52, 3 (2003).
Autore principale: Modha, Dharmendra S.
Natura: Articolo
Lingua:English
Soggetti:
Clustering.
Convexity.
Convex k-means algorithm.
Feature combination.
Feature selection.
Fisher's discriminant analysis.
Text mining.
Unsupervised learning.