Continuous subspace clustering in streaming time series

Performing data mining tasks in streaming data is considered a challenging research direction, due to the continuous data evolution. In this work, we focus on the problem of clustering streaming time series, based on the sliding window paradigm. More specifically, we use the concept of subspace a-clusters. A subspace a-cluster consists of a set of streams, whose value difference is less than a in a consecutive number of time instances (dimensions). The clusters can be continuously and incrementally updated as the streaming time series evolve with time. The proposed technique is based on a careful examination of pair-wise stream similarities for a subset of dimensions and then it is generalized for more streams per cluster. Additionally, we extend our technique in order to find maximal pClusters in consecutive dimensions that have been used in previously proposed clustering methods. Performance evaluation results, based on real-life and synthetic data sets, show that the proposed metho...

• Type: Journal
• Year: 2008
• Where: IS
• Authors: Maria Kontaki, Apostolos N. Papadopoulos, Yannis Manolopoulos
• Keywords: Artificial Intelligence, Continuous Data Evolution, IS 2008, Subspace A-cluster, Time Series