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This paper attempts to explore more precise and intelligent assisted methodological references for the diagnosis and treatment of diabetes in Traditional Chinese Medicine (TCM) by using medical record data from TCM diabetes, and to construct a TCM diabetes assisted diagnosis and treatment framework of “association mining-quantifying calculation-intelligent assisted diagnosis and treatment” what can be described as cluster analyzing and match mining for symptom and drug by association mining, as quantitatively calculating and evaluating for the condition of diabetes; as disease diagnosis, drug recommendation and decision making by intelligent assisted diagnosis and treatment. This paper synthetically employs the technical methodology of association rules, topic clustering, emotion recognition and content recommendation, to realize the knowledge mining, quantify the evaluation and assist decision-making of TCM diabetes, and to propose a reference for the process of consultation and treatment. Overall, the study contributes to the academic and practical value in diabetes by elaborating a new technical methodology and providing a brief empirical test for doctors and researchers.

With the quick increase of information and knowledge, automatically classifying text documents is becoming a hotspot of knowledge management. Standard machine learning techniques like support vector machines(SVM) and related large margin methods have been successfully applied for this task. Unfortunately, the high dimensionality of input feature vectors impacts on the classification speed. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this work is to reduce the dimension of feature vectors, optimizing the parameters to improve the SVM classification accuracy and speed. In order to improve classification speed we spent rough sets theory to reduce the feature vector space. We present a genetic algorithm approach for feature selection and parameters optimization to improve classification accuracy. Experimental results indicate our method is more effective than traditional SVM methods and other traditional methods.

Text classification has been widely used to assist users with the discovery of useful information from the Internet. However, Current text classification systems are based on the “Bag ofWords” (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. To overcome this problem, previous work attempted to enrich text representation by means of manual intervention or automatic document expansion. The achieved improvement is unfortunately very limited, due to the poor coverage capability of the dictionary, and to the ineffectiveness of term expansion. Fortunately, DBpedia appeared recently which contains rich semantic information. In this paper, we proposed a method compiling DBpedia knowledge into document representation to improve text classification. It facilitates the integration of the rich knowledge of DBpedia into text documents, by resolving synonyms and introducing more general and associative concepts. To evaluate the performance of the proposed method, we have performed an empirical evaluation using SVM calssifier on several real data sets. The experimental results show that our proposed framework, which integrates hierarchical relations, synonym and associative relations with traditional text similarity measures based on the BOW model, does improve text classification performance significantly.

Automatic categorization of documents into pre-defined taxonomies is a crucial step in data mining and knowledge discovery. Standard machine learning techniques like support vector machines(SVM) and related large margin methods have been successfully applied for this task. Unfortunately, the high dimensionality of input feature vectors impacts on the classification speed. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this work is to reduce the dimension of feature vectors, optimizing the parameters to improve the SVM classification accuracy and speed. In order to improve classification speed we spent rough sets theory to reduce the feature vector space. We present a genetic algorithm approach for feature selection and parameters optimization to improve classification accuracy. Experimental results indicate our method is more effective than traditional SVM methods and other traditional methods.