K-Fact - Using the Frequency Factor for Clustering Categorical Data.

K-modes, an extension of K-means, aims to cluster categorical data by using the mode to update clustersu0027 centroids, as well an overlap measure to define the distance between objects and centers. This work presents K-fact, an extension of K-modes, which proposes the frequency factor, a new probability-based measure to update the centroids, and the use of several similarity measures suitable to categorical values. In our validity scheme, we ran both K-modes and K-fact over real-world and synthetic datasets in order to compare them with an index from the external validity criteria. K-modes is always outperformed by some parameter set used in K-fact, especially on datasets with higher or moderate variance.