Multivariate Time
نویسندگان
چکیده
EEcient data mining algorithms are crucial for eeective 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 eeciently 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. msdd diiers from other data mining algorithms in the complexity of the structure that it can nd. msdd also requires no domain knowledge to focus or limit its search, although such knowledge is easily incorporated when it is available.
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