Spatial associative classification at different levels of granularity: a probabilistic approach

Authors:
Michelangelo Ceci;Annalisa Appice;Donato Malerba
Affiliations:
Università degli Studi, via Orabona, 4, 70126 Bari, Italy;Università degli Studi, via Orabona, 4, 70126 Bari, Italy;Università degli Studi, via Orabona, 4, 70126 Bari, Italy
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

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Abstract

In this paper we propose a novel spatial associative classifier method based on a multi-relational approach that takes spatial relations into account. Classification is driven by spatial association rules discovered at multiple granularity levels. Classification is probabilistic and is based on an extension of naive Bayes classifiers to multi- relational data. The method is implemented in a Data Mining system tightly integrated with an object relational spatial database. It performs the classification at different granularity levels and takes advantage from domain specific knowledge in form of rules that support qualitative spatial reasoning. An application to real-world spatial data is reported. Results show that the use of different levels of granularity is beneficial.