Data mining with Temporal Abstractions: learning rules from time series |
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Authors: | Lucia Sacchi Cristiana Larizza Carlo Combi Riccardo Bellazzi |
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Affiliation: | (1) Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy;(2) Dipartimento di Informatica, Università degli Studi di Verona, Verona, Italy |
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Abstract: | A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority
of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between
data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such
complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose
a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns
in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest
that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in
the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful
temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper
presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related
to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical
variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between
genes from DNA microarray data. |
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Keywords: | Temporal data mining Rule discovery Temporal abstractions Biomedical time series |
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