Research on Mining Cloud Data Based on Correlation Dimension Feature

Abstract

Large hierarchical cloud storage database has distributed of non-continuous massive data, the data has nonlinear characteristics of strong coupling, and using traditional methods for data mining, mining exist difficult problems. This paper proposes mining algorithm based a cloud non continuous layer data, and analyze the overall data mining model. The paper use fuzzy C means clustering algorithm to complete the semantic ontology feature point clustering beam based on semantic feature extraction and quantization encoding, to realize improved data mining algorithm. The experimental results show that the improved algorithm, the non-continuous mining level data have high precision, good performance, anti-interference ability strong, performance is superior to the traditional method.