A user-satisfaction-based clustering method

Abstract

Clustering is a common method for data analysis where a good clustering helps users to better understand the data. As for clustering quality measurement, the mainly used are some objective measures, while some researchers also paid attention to users' goals and they proposed methods to get users involved in clustering. However, a good clustering must meet the satisfaction of the users. Apart from these objective measures and users' goals, whether the clustering is easy to understand is also important for clustering quality measurement, especially in high-dimensional data clustering, if the data points in the final clusters are with high dimensions, it will hinder users' understanding of the clustering results. With all these concerns considered, we proposed an index of users' satisfaction with high-dimensional data clustering. According to this index, we further put forward a user-satisfaction-based clustering method to better serve users' satisfaction. We first developed an optimization model about users' satisfaction, then we used genetic algorithm to solve this model and obtained some high-quality clusterings, after reclustering of the clusterings obtained in previous steps, a few representative high-quality clusterings are provided for users to select. The experiment results suggest that our method is effective to provide some representative clusterings with the clustering quality, users' goals and the interpretability of clustering results being well considered.