An efficient algorithm for mining time interval-based patterns in large database

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.

[1]  John F. Roddick,et al.  ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data , 2007, Data Knowl. Eng..

[2]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[3]  Suh-Yin Lee,et al.  Fast Discovery of Sequential Patterns by Memory Indexing , 2002, DaWaK.

[4]  Yen-Liang Chen,et al.  Mining Nonambiguous Temporal Patterns for Interval-Based Events , 2007, IEEE Transactions on Knowledge and Data Engineering.

[5]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[6]  Frank Klawonn,et al.  Finding informative rules in interval sequences , 2001, Intell. Data Anal..

[7]  Mohammed J. Zaki,et al.  SPADE: An Efficient Algorithm for Mining Frequent Sequences , 2004, Machine Learning.

[8]  Dimitrios Gunopulos,et al.  Discovering frequent arrangements of temporal intervals , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[9]  Ada Wai-Chee Fu,et al.  Discovering Temporal Patterns for Interval-Based Events , 2000, DaWaK.

[10]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[11]  Umeshwar Dayal,et al.  PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth , 2001, ICDE 2001.

[12]  Mong-Li Lee,et al.  Mining relationships among interval-based events for classification , 2008, SIGMOD Conference.

[13]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[14]  Fabian Mörchen,et al.  Efficient mining of understandable patterns from multivariate interval time series , 2007, Data Mining and Knowledge Discovery.

[15]  Florent Masseglia,et al.  The PSP Approach for Mining Sequential Patterns , 1998, PKDD.