Clustering complex time series databases

Abstract

Time series data account for a large fraction of the data stored in financial, medical and scientific database. As a consequence, in the last decade there has been an explosion of interest in mining time series data and several new algorithms to index, classify, cluster and segment time series have been introduced. In this paper we focus on clustering of time series from a large database provided by a large Italian electric company, and the power consumption of a specific class of power users, that is the business and industrial customers, is measured. The aim of this paper is to propose an effective clustering technique in the frequency domain where the need of computational and memory resources is much reduced in order to make the algorithm efficient for large and complex temporal data bases. © Springer-Verlag Berlin Heidelberg 2011.