Embedding of the Hamming space into a sphere with weighted quadrance metric and c-means clustering of nominal-continuous data

In this paper we present a new c-means clustering algorithm for combined continuous-nominal data. We use spherical representation of nominal data. The impact of specific features is modeled with corresponding weights in metric definition. To solve constrained minimization problem we transfer the me thodology of reformulation functions to the considered context. As a result we obtain a clustering algorithm with adaptation of weights. Series of numerical experiments on real and synthetic data show that the algorithm can successfully cluster raw, non-normalized data.