Incremental mining of temporal patterns in interval-based database. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Hui, Lin; Chen, Yi-Cheng; Weng, Julia; Lee, Suh-Yin
• Is Part Of: Knowledge and information systems. Volume 46:Issue 2(2016:Feb.)
• Page Start: 423
• Page End: 448
• Publication Date: 2016-02
• Subjects: Incremental mining -- Dynamic representation -- Sequential pattern -- Temporal pattern; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0828-5
• Languages: English
• ISSNs: 0219-1377

Abstract

Abstract In several real-life applications, sequence databases, in general, are updated incrementally with time. Some discovered sequential patterns may be invalidated and some new ones may be introduced by the evolution of the database. When a small set of sequences grow, or when some new sequences are added into the database, re-mining sequential patterns from scratch each time is usually inefficient and thus not feasible. Although there have been several recent studies on the maintenance of sequential patterns in an incremental manner, these works only consider the patterns extracted from time point-based data. Few research efforts have been elaborated on maintaining time interval-based sequential patterns, also calledtemporal patterns, where each datum persists for a period of time. In this paper, an efficient algorithm, Inc_TPMiner (Incremental Temporal Pattern Miner ) is developed to incrementally discover temporal patterns from interval-based data. Moreover, the algorithm employs some optimization techniques to reduce the search space effectively. The experimental results on both synthetic and real datasets indicate thatInc_TPMiner significantly outperforms re-mining with static algorithms in execution time and possesses graceful scalability. Furthermore, we also applyInc_TPMiner on a real dataset to show the practicability of incremental mining of temporal patterns.