One way of representing semantics could be via a high dimensional conceptual space constructed by certain lexical semantic space models. Concepts (words), represented as a vector of other words in the semantic space, can be categorized via clustering techniques into a number of regions reflecting different contexts. The conventional clustering algorithms, e.g., K-means method, however, normally...

This paper presents a generalized version of the conventional k-means clustering algorithm [Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, University of California Press, Berkeley, 1967, p. 281]. Not only is this new one applicable to ellipse-shaped data clusters without dead-unit problem, but also performs correct clustering without pre-assigning the exact...