A New Multivariate Time Series Transformation Technique Using Closed Interesting Subspaces

Abstract

Subspace clustering detects the clusters that are existing in the subspaces of the feature space. Density based subspace clustering defines clusters as regions of high density existing in subspaces of multidimensional datasets. This paper discusses the concept of closed interesting subspaces under density divergence context for multivariate datasets and proposes an algorithm to transform the multivariate time series to a symbol sequence using the closed interesting subspaces. The proposed transformation allows the applicability of any of the symbolic sequential mining algorithms to efficiently extract sequential patterns which capture the interdependencies and co-variations among groups of time series variables. The multivariate time series transformation technique is explained using a sample dataset. It is evaluated using a real world weather dataset obtained from Cambridge University. The representation power of the closed interesting subspaces and maximal interesting subspaces in transforming multivariate time series is compared.