Abstract. Here the issue of discovery of frequent multi-dimensional patterns from relational sequences is addressed. The great variety of applications of sequential pattern mining makes this problem one of the central topics in data mining. Nevertheless, sequential information may concern data on multiple dimensions and, hence, the mining of sequential patterns from multi-dimensional information results very important. This work takes into account the possibility to mine complex patterns, expressed in a first-order language, in which events may occur along different dimensions. Specifically, multi-dimensional patterns are defined as a set of atomic first-order formulae in which events are explicitly represented by a variable and the relations between events are represented by a set of dimensional predicates. A complete framework and an Relational Learning algorithm to tackle this problem are presented along with some experiments on artificial and real multi-dimensional sequences.
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