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The emergency of a new computing pattern must be supported by powerful mathematical theory, weak theoretical supported ideology does not have a strong vitality, and which will become vulnerable. As being a new theory, the DNA computing treated as a new computational pattern also need a lot of basic theoretical research system. This article describes the nature of DNA computing, given the equivalence of DNA computing pattern with traditional computer under the support of automata theory system, further analysis and understanding the DNA computing pattern.

In 2012, Wang Zuofei built up granularity-function and applied it to the measure of attribute importance and attribute reduction. On this basis, granularity-function based upon pessimistic and optimistic multi-granularity rough set is constructed. It is applied to the calculation of attribute importance and attribute reduction. According to the experimental results, the method can reduce the dimension of features and obviously improve the classification accuracy and efficiency.

KNN classifier is a simple, non-parametric, high efficiency algorithm. But it has the defect that the classification efficiency will increase as the enlargement of data scale. This paper put forward a new KNN classification method based on rough set on the research of algorithms presented by Yu Ying and Heping Gou. The innovation of this algorithm is that the kernel, negative domain and boundary region of variable precision rough set are used to be the indexes of measuring the intra-class, extra-class and the class boundary of training sample set. The samples of intra-class, extra-class and the class boundary that are to be classed have differential treatment when judging the category. In this way, the scale of training sample set is effectively reduced and the efficiency and precision of classification are improved. At last, the category function is improved to reflect the category of samples to be classed. The experimental results show that the method can obviously improve the classification efficiency and accuracy.