TY - GEN
T1 - An efficient algorithm for mining time interval-based patterns in large databases
AU - Chen, Yi Cheng
AU - Jiang, Ji Chiang
AU - Peng, Wen-Chih
AU - Lee, Suh Yin
PY - 2010
Y1 - 2010
N2 - Most studies on sequential pattern mining are mainly focused on time point-based event data. Few research efforts have elaborated on mining patterns from time interval-based event data. However, in many real applications, event usually persists for an interval of time. Since the relationships among event time intervals are intrinsically complex, mining time interval-based patterns in large database is really a challenging problem. In this paper, a novel approach, named as incision strategy and a new representation, called coincidence representation are proposed to simplify the processing of complex relations among event intervals. Then, an efficient algorithm, CTMiner (Coincidence Temporal Miner) is developed to discover frequent time-interval based patterns. The algorithm also employs two pruning techniques to reduce the search space effectively. Furthermore, experimental results show that CTMiner is not only efficient and scalable but also outperforms state-of-the-art algorithms.
AB - Most studies on sequential pattern mining are mainly focused on time point-based event data. Few research efforts have elaborated on mining patterns from time interval-based event data. However, in many real applications, event usually persists for an interval of time. Since the relationships among event time intervals are intrinsically complex, mining time interval-based patterns in large database is really a challenging problem. In this paper, a novel approach, named as incision strategy and a new representation, called coincidence representation are proposed to simplify the processing of complex relations among event intervals. Then, an efficient algorithm, CTMiner (Coincidence Temporal Miner) is developed to discover frequent time-interval based patterns. The algorithm also employs two pruning techniques to reduce the search space effectively. Furthermore, experimental results show that CTMiner is not only efficient and scalable but also outperforms state-of-the-art algorithms.
KW - Data mining
KW - Interval-based mining
KW - Sequential pattern
KW - Temporal pattern
UR - http://www.scopus.com/inward/record.url?scp=78651275243&partnerID=8YFLogxK
U2 - 10.1145/1871437.1871448
DO - 10.1145/1871437.1871448
M3 - Conference contribution
AN - SCOPUS:78651275243
SN - 9781450300995
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 49
EP - 58
BT - CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
T2 - 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
Y2 - 26 October 2010 through 30 October 2010
ER -