Khalid Abdulla, Andrew Wirth, et al.
ICIAfS 2014
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 (and possibly different) distortion measure to each feature space, (iii) to combine distortions on different feature spaces, in a convex fashion, by assigning (possibly) different relative weights to each, (iv) for a fixed weighting, to cluster using the proposed convex k-means algorithm, and (v) to determine the optimal feature weighting to be the one that yields the clustering that simultaneously minimizes the average within-cluster dispersion and maximizes the average between-cluster dispersion along all the feature spaces. Using precision/recall evaluations and known ground truth classifications, we empirically demonstrate the effectiveness of feature weighting in clustering on several different application domains.
Khalid Abdulla, Andrew Wirth, et al.
ICIAfS 2014
Els van Herreweghen, Uta Wille
USENIX Workshop on Smartcard Technology 1999
Bing Zhang, Mikio Takeuchi, et al.
NAACL 2025
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008