摘要： 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 called temporal 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 that Inc_TPMiner significantly outperforms re-mining with static algorithms in execution time and possesses graceful scalability. Furthermore, we also apply Inc_TPMiner on a real dataset to show the practicability of incremental mining of temporal patterns. 其他題名： Knowl Inf Syst 出版者： London: Springer London 出版日期： 2016-02 出處： Knowledge and information systems, 2016-02, Vol.46 (2), p.423-448 資源來源： ABI/INFORM Collection 版權： Springer-Verlag London 2015 版權： Springer-Verlag London 2016 識別號： ISSN: 0219-1377 識別號： EISSN: 0219-3116 識別號： DOI: 10.1007/s10115-015-0828-5 識別號： CODEN: KISNCR
