Nominal time series representation for the clustering problem

Abstract

In this paper we considered time series dimension reduction for clustering problem. The techniques of reduction of dimension of time series is based on the concept of envelopes, aggregation of the envelopes and extracting essential attributes. Essential attributes were nominalized. The reduced representation of time series is characterized by nominal attributes. For such representation of time series we applied a definition of conditions domination within each pair of clusters. We proposed a hierarchical agglomerative approach to clustering nominal data. There is considered a case of data series clustering problem as an illustrative example.