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Contextual information refers to all the factors affecting the recommendation system and supporting recommendation except “user - item” assessment information. To make effective recommendation to the user against a specific context, it is very important to integrate contextual information into recommendation. Based on contextual information, this paper improves collaborative filtering recommendation algorithm, applying the algorithm to the curricula-variable recommendation context so as to improve the accuracy of recommendation results and provide certain guidance and suggestions to curricula-variable process.

To explore the essential characteristic of TCM syndromes for Chronic pulmonary heart disease (CPHD) The CPHD subjects were recruited from four TCM hospitalsrespirations department by means of clinical epidemiological method.The research dates were inputted with Epidata ver.3.1 and analyzed with dynamic Kohonen Network and Decision Tree adopted to analysis the common syndrome for CPHD. There were six common rules before rules transformed and main symptom and minor symptom for common syndrome setted. The diagnostic accuracy rate was72.2% The data mining technology could been used into the TCM syndrome characteristic research.

This paper explores a method for establishing diagnostic criterion of TCM (traditional Chinese medicine) syndrome differentiation by studying the set-up of diagnostic standard model of syndrome differentiation of Type 2 diabetes. We present, based on dynamic Kohonen network, the artificial neural net (ANN), fuzzy system (FS) that were used to build up adaptive fuzzy inference system model. The system was tested for its reliability via fisher-iris determination. Then with the help of this model the clinical data was studied, simultaneously, according to TCM basic theories. We obtained the diagnostic criterion of commonly-seen syndrome of Type 2 diabetes, whose rationality was verified by data check-up.

In allusion to the feature selection of multiclass problems, a generalized attribute reduction method based on between-class distance is proposed. In the new method, the interclass separability is described by the tolerance relation and distance matrix defined by Bhattacharyya distance. Some attribute reduction problems were investigated on generalized approximation space, which make use of the tolerance relation and the matrix. Based on the Boolean reasoning and the monotony of the distance function, a feasible algorithm for feature selection is presented. Compared with traditional principle component transformation (PCT) approach, practical experiment result shows that the proposed method can prominently improve the accuracy of classification and generalization on hypersepectral remote sensing images.