Abstract In the Bayesian non-parametric models, normalized random measures have significant yield of discrete random measures; where some well-established processes are applied. The posterior inference models use the methods which are easy to be applied and the convergence becomes more usable. The incremental Bayesian hierarchical clustering is created for NRM mixture models. While doing so it is important to consider the... In the Bayesian non-parametric models, normalized random measures have significant yield of discrete random measures; where some well-established processes are applied. The posterior inference models use the methods which are easy to be applied and the convergence becomes more usable. The incremental Bayesian hierarchical clustering is created for NRM mixture models. While doing so it is important to consider the efficiency and application of online inferences. We in this work have introduced a hybrid inference algorithm for the mixture models which ensures the benefits of mixture models and Bayesian hierarchical clustering. The proposed trees are ensured for better coverage and use fast convergence to the tree proposals. We have tested the proposal and found the better outcome based both on synthetic and real-world datasets. Read More Read Less

Abstract Cloud applications have big impact on electronic learning and language learning process. The proposed framework in this paper is very generic aimed at the systems teaching and web-based networks. We hope that the general framework can be customized to suit individual applications.... Cloud applications have big impact on electronic learning and language learning process. The proposed framework in this paper is very generic aimed at the systems teaching and web-based networks. We hope that the general framework can be customized to suit individual applications. Read More Read Less