A Novel Algorithm for Mining Closed Temporal Patterns from Interval-Based Data
學年 104
學期 1
出版(發表)日期 2016-01-01
作品名稱 A Novel Algorithm for Mining Closed Temporal Patterns from Interval-Based Data
作品名稱(其他語言)
著者 Chen, Yi-Cheng; Weng, Julia Tzu-Ya; Hui, Lin
單位
出版者
著錄名稱、卷期、頁數 Knowledge and Information Systems 46(1), pp.151-183
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 Closed Coincidence 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.
關鍵字 Data mining;Closed sequential pattern;Closed temporal pattern;Coincidence representation
語言 en
ISSN 0219-1377;0219-3116
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Chen, Yi-Cheng
審稿制度
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107568 )