SAT-MOD: moderate itemset fittest for text classification.

In this paper, we present a novel association-based method called SAT-MOD for text classification. SAT-MOD views a sentence rather than a document as a transaction, and uses a novel heuristic called MODFIT to select the most significant itemsets for constructing a category classifier. The effectiveness of SAT-MOD has been demonstrated comparable to well-known alternatives such as LinearSVM and much better than current document-level words association based methods on the Reuters corpus.