In recent years, there has been a rise in the popularity of probabilistic (Bayesian) approaches for clustering and pattern recognition. This is due to the development of new probabilistic programming languages and learning algorithms, as well as an overall increase in optimization problems. These approaches aim to solve problems in domains where only a small amount of data from each class can be labeled.

Clustering analysis is a statistical method for studying pattern recognition problems in data mining. My goal for the next few years is to develop a clustering or tensor-based clustering method for pattern recognition. This method can be applied to single or multiple view or resource data.