Research On Mulitple Time Series Streams Clustering Algorithms

Along with the requirements of data streams' applications increasing, clustering algorithms on data streams attract more and more researchers' interests. The paper aims at multiple time series streams, brings forward the method to compute multilayer correlation, and brings forward mining algorithms for multi-streams on the base of multilayer correlation.Multi-streams' multilayer correlation computing method have three advantages: (1)Multi-streams' multilayer correlation computing method improves the classic time series correlation methods , adapting to streams variety and the character of can not repeat;(2)We use DFT to compress and deal with data streams' data, it's reducing the requirements of system's memory storage, quickening computer's deal time;(3)We use multilayer time windows model to statistic multilayer correlations. We not only know correlations between current data streams, but also can query history correlations between multi-streams by DSMGA-Tree, returning multilayer correlations, meeting particular query request under a certain error range.On the base of multilayer correlation, we bring forward dynamic mining algorithms for multi-streams. (1)Algorithms use classic mining techniques in databases to multi-streams, achieve multi-streams clustering dynamical; (2) We propose CBDMSClustering on the base of correlation , use DBSCAN's idea on the base of density, can gain arbitrary clusters, meanwhile without add time complexity;(3)On the base of CBDMSClustering, we improve further, propose RCBDMS Clustering. It can distinguish different correlation density clusters, other than gain arbitrary clusters. It can identify high correlation clusters which covered by low correlation clusters. Experiments prove that the multi-streams dynamic clustering algorithms advances result obviously.