An improved k-prototypes clustering algorithm for mixed numeric and categorical data
We propose a new representation for the prototype of a cluster with mixed attributes. We give a new measure to assess the dissimilarity between data objects and prototype. This measure considers the significance of attribute towards clustering process. Our algorithm can calculate the significance of attribute towards clustering. Our algorithm achieves better results according to the clustering accuracy.
clustering data mining mixed data dissimilarity measure attribute significance