Abstract: Big data clustering process is a kind of stochastic nonlinear processing and has very high uncertainty.Because the traditional methods need prior knowledge to learn,are not good to adapt to the real-time change situation of big data and unable to effectively implement large data clustering,we put forward a kind of big data clustering method based on chaotic correlation feature extraction.We analyzed the disadvantages of the traditional methods,established a multidimensional state space vector and the chaotic trajectory by phase space reconstruction.Much of the geometry characte-ristic information in the original system remains same,which provides the effective basis for the analysis of chaotic cha-racteristics of the original system.Time delay referred by the abscissa when the average mutual information obtains the first minimum is as the best time delay of reconstructing phase space,and the false nearest neighbor algorithm is used to select the best embedding dimension.The extracted correlation dimension is used as the haotic correlation characteristics of bige data clustering,and big data is clustered based on the extracted chaos correlation dimension feature .The simulation results show that the proposed algorithm can effectively improve the efficiency of the clustering of data,reduce energy consumption,and is an effective method of data clustering.

Key words: Chaos correlation dimension feature,Big data,Clustering