Detecting the Number of Clusters in n-Way Probabilistic Clustering

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.

近期，人们对多路概率聚类的兴趣日益增长。针对这一问题，已经开发出了一些有效的算法。然而，对于一般的n路聚类（n≥2）如何检测聚类数量，人们关注得并不多。为了填补这一空白，本文基于n路代数理论对这一问题进行了研究。通过特征值分解（EVD）提出了一种简单而有效的检测方法，该方法易于实现。我们对这一方法进行了论证。此外，通过在模拟数据集和真实世界数据集上的实验，证明了其有效性。