Research Of Clustering Algorithm In Information Value-added

With the development of the technology of getting the information, there is more and more information in all fields. After the information's value is added, it can play a more important role in our society. So it is important to study on how to make the information more valuable. Nowadays, Data Mining is the broadest way to do this. In this paper, we raise the point that clustering algorithm is the most efficient way to make the information's value add.However, the existing clustering algorithm has two shortcomings: The first one is that the clustering algorithm depends on the initial given input so much. In the paper, we propose an algorithm of finding the initial input based on the sub-sample theory. According to the test data, the novel algorithm not only decreases the sensitivity but also generates better quality clusters. The second shortcoming is that the clustering algorithm can not solve the dynamic data very well. In this paper we develop another new algorithm to improve it. According to the ant system, the distributing of the original data is gathered into a degree constraint tree. Then the clusters can be generated according to the feature of the tree. We analyze the result of the algorithm on the test data. It shows that the novel algorithm is an efficient way to cluster the dynamic data.In the last part of this paper, we expatiate how important of adding the value of the scores of entrance-test-to-college. And then after the proposed algorithm works on the scores, we give some useful conclusions of the scores.