Mining Frequent Closed Ordered Subtrees under Anti-monotone Constraints by using Restricted Occurrence Matching

Abstract

As semi-structured data is used widely in several fields, the importance of structured data mining is increasing recently. Although mining frequent patterns in structured data is one of the most fundamental tasks, frequent pattern miners often discover huge number of patterns. To overcome this problem, two major approaches, condensed representation mining and constraint-based mining, have been proposed. In this paper, as a technique for integrating these two approaches, we propose three algorithms, RC_LOCOT, posC_LOCOT, and negC_LOCOT, for discovering closed ordered subtrees under anti-monotone constraints about the structure of patterns to be discovered. The proposed algorithms discover closed constrained subtrees efficiently not by post-processing but by pruning and skipping the search space based on the occurrence matching and the patterns on the border.