Conventional soft partition based clustering algorithms usually cannot achieve desired clustering outcomes in the situations where the data are quite spare or distorted. To address this problem, based on maximum entropy clustering, by means of the strategy of historical knowledge learning, two novel soft partition based clustering models called SPBC-RHK-1 and SPBC-RHK-2 for short respectively were proposed. SPBC-RHK-1 is the basic model which only refers to the historical cluster centroids, whereas SPBC-RHK-2 is of advanced modality based on the combination of historical cluster centroids and historical memberships. In terms of the historical knowledge, the effectiveness of both algorithms was improved distinctly, and SPBC-RHK-2 method showed better effectiveness and robustness compared to the other method since its higher ability of utilizing knowledge. In addition, because the involved historical knowledge does not expose the historical raw data, both of these two approaches have good capacities of privacy protection for historical data. Finally, experiments were conducted on both artificial and real-world datasets to verify above merits.

The classical regression systems modeling methods suppose that the training data are sufficient, but partial information missing may weaken the generalization abilities of the regression systems constructed based on this dataset. In order to solve this problem, a regression system with the transfer learning abilities, i.e. Transfer learning Support Vector Regression (T-SVR for brevity) was proposed based on support vector regression. T-SVR could use the current data information sufficiently, and learn from the existing useful historical knowledge effectively, so that remedy the information lack in the current scene. Reinforced current model was obtained through controlling the similarity between current model and history model in the object function and current model can benefit from history scene when information is missing or insufficient. The experiments on simulation data and real data show that T-SVR has better adaptability than the traditional regression modeling method in the scene with information missing.