Springer London;London: Springer Science and Business Media LLC
摘要:
摘要： Closed sequential patterns have attracted researchers’ attention due to their capability of using compact results to preserve the same expressive power as conventional sequential patterns. However, studies to date have mainly focused on mining conventional patterns from time interval-based data, where each datum persists for a period of time. Few research efforts have elaborated on discovering closed interval-based sequential patterns (also referred to as closed temporal patterns). Mining closed temporal patterns are an arduous problem since the pairwise relationships between two interval-based events are intrinsically complex. In this paper, we develop an efficient algorithm, CCMiner , which stands for C losed C oincidence Miner to discover frequent closed patterns from interval-based data. The algorithm also employs some optimization techniques to effectively reduce the search space. The experimental results on both synthetic and real datasets indicate that CCMiner not only significantly outperforms the prior interval-based mining algorithms in execution time but also possesses graceful scalability. Furthermore, we also apply CCMiner to a real dataset to show the practicability of time interval-based closed pattern mining. 其他題名： Knowl Inf Syst 出版者： London: Springer Science and Business Media LLC 出版日期： 2016-01-01 出處： Knowledge and Information Systems, 2016-01, Vol.46 (1), p.151-183 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/s10115-014-0815-2 識別號： CODEN: KISNCR
