Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach

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 naïve 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.

• Type: Conference
• Year: 2004
• Where: PKDD
• Authors: Michelangelo Ceci, Annalisa Appice, Donato Malerba
• Keywords: Granularity Levels, PKDD 2004, Spatial, Spatial Associative Classifier