Classification Algorithm of Multivariable Data Stream Based on Motifs and Their Temporal Motifs Relations
Sun Yue-yao · Journal of Intelligence · 2012
Gaining increasing concerns,the precise classification problems of multivariate data stream is currently the hot and difficult area of data mining and information science among research groups.But the previous studies are mostly dependent on individual flow feature extraction and classification,while the characteristics of interdependence between data streams are not considered.Based on these,using bioinformatics motif-searching method,the article proposes a classification method of the term frequency and inverse document frequency.The method is mainly to translate each input stream into the sequence of symbols to describe the feature of signal change,and the symbol is divided into different block length in order to effectively extract the motif.By calculating the motif frequency,long term frequency and inverse document frequency weight,the temporal motifs of motifs are measured between the different input data streams,and the relationship of motif and the temporal motif between the multivariable data is used to classify multivariable data stream,then the simulation results show that the method is effective.