A spatiotemporal view of transactional data for data mining

Authors:
Ioannis N. Kouris;Christos H. Makris;Athanasios K. Tsakalidis
Affiliations:
Department of Computer Engineering and Informatics, School of Engineering, University of Patras, Patras, Hellas, Greece and Computer Technology Institute, Patras, Hellas, Greece;Department of Computer Engineering and Informatics, School of Engineering, University of Patras, Patras, Hellas, Greece and Computer Technology Institute, Patras, Hellas, Greece;Department of Computer Engineering and Informatics, School of Engineering, University of Patras, Patras, Hellas, Greece and Computer Technology Institute, Patras, Hellas, Greece
Venue:
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Year:
2005

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Abstract

Transactional-retail data, probably the data with the largest scientific and financial interest, still remains the most examined data category in the field of data mining. The prominent model for handling such data has long been a rather naïve one that treated items as flat Boolean variables, ordered in various ways in order to facilitate efficient counting and to reduce complexity. In this work we propose a novel view of transactional data for data mining, which is based on a spatial, a temporal or a spatiotemporal component inherent in such data. Finally we propose a method for handling this data efficiently.