Time Series Clustering via NMF in Networks

Abstract

Time series data mining has attracted a lot of attention in the last decade, especially the research on the clustering of time series data. Network-based clustering technology, transforming data of time series into a network and then used community detection methods of network to cluster time series, is a new approach to cluster time series data. This approach takes the advantage that a network can describe the relationship between any pair or any group of data samples, but the effectiveness of clustering heavily dependent on the performance of algorithms of community detection. In this paper, we cluster time series by transforming them into network and detecting communities by non-negative matrix factorization (NMF). Experimental evaluations illustrate the superiority of our approach compared with the state-of-the-arts such as Multilevel.