An Adaptive Three-Way Clustering Algorithm for Mixed-Type Data

The three-way clustering is different from the traditional two-way clustering. Instead of using two regions to represent a cluster by a single set, a cluster is represented by a pair of sets, and there are three regions such as the core region, fringe region and trivial region. The three-way representation intuitively shows that which objects are fringe to the cluster and it is proposed for dealing with uncertain clustering. However, the three-way clustering algorithm usually needs an appropriate evaluation function and corresponding thresholds. It is not scientific and efficient method for setting the thresholds in advance. Meanwhile, there is a large amount of mixed-type data in real life. Therefore, this paper proposes an adaptive three-way clustering algorithm for mixed-type data, which adjusts the three-way thresholds during the clustering process based on the idea of universal gravitation by excavating more detailed ascription relation between objects and clusters. The experimental results show that the proposed algorithm has good performance in indices such as the accuracy, F-measure, RI and NMI.