Multi-Dimensional Relational Sequence Mining

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 from complex 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 ...

• Type: Journal
• Year: 2008
• Where: FUIN
• Authors: Floriana Esposito, Nicola Di Mauro, Teresa Maria Altomare Basile, Stefano Ferilli
• Keywords: Frequent Multi-dimensional Patterns, FUIN 2008, Multi-dimensional, Sequential Patterns