Clustering Algorithms for Large Temporal Data Sets

Temporal Data Mining is a rapidly evolving and new area of research that is at the intersection of several disciplines, including statistics, temporal pattern recognition, optimisation, visualisation, high-performance computing, and parallel computing. This paper is intended to serve a discussion on a specific Temporal Data Mining task: Temporal Cluster Analysis. Most clustering algorithms of the traditional type are severely limited in dealing with large temporal data sets. Therefore we discuss the applicability of clustering algorithms to these data sets. This paper is enriched with an application of a new algorithm on a real sequential database.