Two scalable algorithms for associative text classification

Associative classification methods have been recently applied to various categorization tasks due to its simplicity and high accuracy. To improve the coverage for test documents and to raise classification accuracy, some associative classifiers generate a huge number of association rules during the mining step. We present two algorithms to increase the computational efficiency of associative classification: one to store rules very efficiently, and the other to increase the speed of rule matching, using all of the generated rules. Empirical results using three large-scale text collections demonstrate that the proposed algorithms increase the feasibility of applying associative classification to large-scale problems.

► Many association rules have advantage in the classifier build process.
► A large number of rules take much space and time to process.
► We suggest a compact representation method of rules.
► We suggest a fast rule matching algorithm.
► Our methods enable large-scale datasets to be used in the classification.