Parallel and Distributed Search for Structure in Multivariate Time Series

Abstract. E cient data mining algorithms are crucial fore ective knowledge discovery. We present the Multi-Stream Dependency Detection (msdd) data mining algorithm that performs a systematic search for structure in multivariate time series of categorical data. The systematicity of msdd's search makes implementation of both parallel and distributed versions straightforward. Distributing the search for structure over multiple processors or networked machines makes mining of large numbers of databases or very large databases feasible. We present results showing that msdd e ciently nds complex structure in multivariate time series, and that the distributed version nds the same structure in approximately 1=n of the time required by msdd, where n is the number of machines across which the search is distributed.

• Type: Conference
• Year: 1997
• Where: ECML
• Authors: Tim Oates, Matthew D. Schmill, Paul R. Cohen
• Keywords: Data Mining Algorithm, Distributed Version, ECML 1997, Machine Learning, Multivariate Time Series