Detecting the Number of Clusters in n-Way Probabilistic Clustering
Zhaoshui He, Andrzej S Cichocki, Shengli Xie, Kyuwan Choi · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2010
Recently, there has been a growing interest in multiway probabilistic clustering. Some efficient algorithms have been developed for this problem. However, not much attention has been paid on how to detect the number of clusters for the general n-way clustering (n ≥ 2). To fill this gap, this problem is investigated based on n-way algebraic theory in this paper. A simple, yet efficient, detection method is proposed by eigenvalue decomposition (EVD), which is easy to implement. We justify this method. In addition, its effectiveness is demonstrated by the experiments on both simulated and real-world data sets.