Amarachi Blessing Mbakwe, Joy Wu, et al.
NeurIPS 2023
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.
Amarachi Blessing Mbakwe, Joy Wu, et al.
NeurIPS 2023
Vijay K. Naik, Sanjeev K. Setia, et al.
Journal of Parallel and Distributed Computing
Ryan Johnson, Ippokratis Pandis
CIDR 2013
Yehuda Naveli, Michal Rimon, et al.
AAAI/IAAI 2006