A novel algorithm for mining closed temporal patterns from interval-based data

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/106262

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106262

Title:	A novel algorithm for mining closed temporal patterns from interval-based data
Authors:	陳以錚;Chen, Yi-Cheng;Weng, Julia Tzu-Ya;Hui, Lin
Contributors:	管理學院資訊管理學系
Keywords:	Algorithms;Analysis;Computer Science;Data mining;Data Mining and Knowledge Discovery;Database Management;Enzymes;Information Storage and Retrieval;Information systems;Information Systems and Communication Service;Information Systems Applications (incl.Internet);IT in Business;Miners;Optimization;Optimization techniques;Pattern recognition;Preserves;Regular Paper;Stands;Studies;Temporal logic
Date:	2016-01-01
Issue Date:	2026-04-23 13:15:37 (UTC+8)
Publisher:	Springer London;London: Springer Science and Business Media LLC
Abstract:	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
Appears in Collections:	[Department of Information Management] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	19	View/Open

社群 sharing

Loading...