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...

Full beskrivning

Bibliografiska uppgifter

I publikationen:	Machine learning. 52, 3 (2003).
Huvudupphovsman:	Modha, Dharmendra S.
Materialtyp:	Artikel
Språk:	English
Ämnen:	Clustering. Convexity. Convex k-means algorithm. Feature combination. Feature selection. Fisher's discriminant analysis. Text mining. Unsupervised learning.