Mining Temporal Patterns in Time Interval-Based Data
學年 104
學期 1
出版（發表）日期 2015-12-01
作品名稱 Mining Temporal Patterns in Time Interval-Based Data
作品名稱（其他語言）
著者 Chen, Yi-Cheng; Peng, Wen-Chih; Lee, Suh-Yin
單位
出版者
著錄名稱、卷期、頁數 IEEE Transactions on Knowledge and Data Engineering 27(12), p.3318-3331
摘要 Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship between two intervals is intrinsically complex, how to effectively and efficiently mine interval-based sequences is a challenging issue. In this paper, two novel representations, endpoint representation and endtime representation, are proposed to simplify the processing of complex relationships among event intervals. Based on the proposed representations, three types of interval-based patterns: temporal pattern, occurrence-probabilistic temporal pattern and duration-probabilistic temporal pattern, are defined. In addition, we develop two novel algorithms, Temporal Pattern Miner (TPMiner) and Probabilistic Temporal Pattern Miner (P-TPMiner), to discover three types of interval-based sequential patterns. We also propose three pruning techniques to further reduce the search space of the mining process. Experimental studies show that both algorithms are able to find three types of patterns efficiently. Furthermore, we apply proposed algorithms to real datasets to demonstrate the effectiveness and validate the practicability of proposed patterns.
關鍵字 data mining;interval-based event;representation;sequential pattern;temporal pattern
語言 en_US
ISSN 1041-4347
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者 Chen, Yi-Cheng
審稿制度 是
國別 USA
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/987654321/106215 )