Affiliations: Università degli Studi di Bari, Dipartimento di
Informatica, 70125 Bari, Italy. {esposito,ndm,basile,ferilli}@di.uniba.it
Note: [] Address for correspondence: Università degli Studi di Bari,
Dipartimento di Informatica, 70125 Bari, Italy
Abstract: The issue addressed in this paper concerns the discovery of frequent
multi-dimensional patterns from relational sequences. The great variety of
applications of sequential pattern mining, such as user profiling, medicine,
local weather forecast and bioinformatics, 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. In a multi-dimensional
sequence each event depends on more than one dimension, such as in
spatio-temporal sequences where an event may be spatially or temporally related
to other events. In literature, the multi-relational data mining approach has
been successfully applied to knowledge discovery fromcomplex data. However,
there exists no contribution to manage the general case of multi-dimensional
data in which, for example, spatial and temporal information may co-exist. 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, multidimensional 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 Inductive Logic Programming algorithm
to tackle this problem are presented along with some experiments on artificial
and real multi-dimensional sequences proving its effectiveness.
