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...

詳細記述

書誌詳細

出版年:	Machine learning. 52, 3 (2003).
第一著者:	Modha, Dharmendra S.
フォーマット:	論文
言語:	English
主題:	Clustering. Convexity. Convex k-means algorithm. Feature combination. Feature selection. Fisher's discriminant analysis. Text mining. Unsupervised learning.