Bi-clustering is a useful approach in analyzing large biological data sets when the observations come from heterogeneous groups and have number of features. We outline general Bayesian tackling bi-clustering problems moderate to high dimensions propose three models on categorical which increase complexities their modeling distributions features across bi-clusters. Our proposed methods apply wid...

Abstract Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of clustering. Although a weighted $$L_1$$ L 1 norm usually employed for regularization term sparse clustering, its use increases dependence on data...

Patent analysis is an important approach to technology forecasting because patents are an important component of developing technology. Also, we use the results of technology forecasting to build the R&D strategies efficiently. In this paper, we consider patent clustering as one of patent analyses. That is, we cluster patent documents in order to forecast the vacant area of a given technology f...