Research Of Higher Vocational Student's Non-intellectual Factors Based On Rough Set

In recent years, the enrollment scale of universities is expanded, its quality of education issues attracted wide attention, especially in vocational colleges, students in an annual decline in the quality of students, and the quality of education is even more worrying. An intellectual level of students is not much difference, what are the factors that directly affect the student's grade to become the key? It is Very worth our studying.Rough Set (Rough Sets, RS) theory is based on the Polish mathematician Z. Pawlak, represented by the researchers proposed a characterization of incomplete and non-deterministic mathematical tools and is a very effective method of data mining. Rough set theory is the core content of knowledge reduction. Discernibility matrix and discernibility function are a powerful tool seeking nuclear attributes and attribute reduction.In this paper, rough set theory in the analysis and deal with uncertain, inconsistent information, such as the unique advantages, according to student achievement related to the characteristics of uncertain factors, the author carried out research of vocational student's non-intellectual factors based on rough set.In this paper, firstly, the algorithms of attribute reduction are researched; they are algorithm based on discernibility matrix, heuristic reduction algorithm based on importance of attribute and improving algorithm based on discernibility matrix. A new improving algorithm on discernibility matrix is put forward, it can suit for compatible resolution lists and incompatible ones, improve the algorithm of attribute reduction and decrees complexity of time; it stressed the importance of algorithm of attribute value reduction, improve the algorithm of attribute value reduction, deal with possible duplication and incompatible with the rules and decrees complexity of time.The author collected a large number data on influencing students learning achievement of higher vocational non-intellectual factors of, and collated data to form a decision table. Application of this new improving DMNI algorithm, carries out data mining, then forms a decision-making rules. It not only provides the scientific basis for the reform of vocational education, but certainly has directive significance for the higher vocational colleges and students management.