Data mining with Temporal Abstractions: learning rules from time series
Data Mining and Knowledge Discovery
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In this paper we introduce a novel method to extract from data and graphically represent the temporal relationships between events, called Precedence Temporal Network. The new approach first derives events from time series by exploiting the temporal abstraction technique, then derives temporal precedence between abstractions in terms of association rules and finally expresses the relationships as a labeled graph. The method is applied to the problem of representing the temporal behavior of gene expressions, as they are collected by DNA microarrays. In particular, in this paper we present the results obtained from the analysis of the expression of a subset of the genes involved in cell-cycle regulation.