Research on Optimization of Discontinuous Data Path Mining Based on Fuzzy Clustering Algorithm

Discontinuous data is a kind of large-scale hierarchical data. Generally speaking, this kind of data has time attribute, and it hopes to show some time-varying characteristics on the basis of keeping the hierarchical structure clear. Using path mining to analyze discontinuous data paths in cloud computing environment, we can find the location and data state of discontinuous data paths. Classifying and counting discontinuous data is beneficial to the efficient use of data. In this paper, the optimization of discontinuous data path mining based on fuzzy clustering algorithm is studied. In order to optimize the path mining of discontinuous data based on fuzzy clustering algorithm, this paper extracts the semantic directional features of discontinuous data and quantifies them. On the basis of quantization coding, FCM (fuzzy C means) algorithm is adopted to realize the directional beam clustering of semantic ontology features and improve the path mining algorithm. The research results show that the proposed algorithm is used to optimize the path mining of discontinuous data based on fuzzy clustering algorithm, and the root mean square error of the path mining output is low, which shows that the path mining accuracy is higher than the traditional method and the anti-interference performance is strong.