Analysis on Network Clustering Algorithm of Data Mining Methods Based on Rough Set Theory

Abnormal data mining algorithm is proposed on the basis of clustering algorithm of isolated point factor. On the one hand the abnormal data can be found in large amounts of data, on the other hand, it also improves the accuracy of clustering. At the same time, it uses a mining algorithm that bases on the forward approximate decision rule and conducts the research to the coordinated decision table by using equivalence relation race which has partial ordering relation. Thus it has carried on the decision rule mining dynamically. The results show that the data mining method based on rough set theory can optimize the clustering algorithm in network data.