Abstract
In several real-life applications, sequence databases, in general, are updated incrementally with time. Some discovered sequential patterns may be invalidated and some new ones may be introduced by the evolution of the database. When a small set of sequences grow, or when some new sequences are added into the database, re-mining sequential patterns from scratch each time is usually inefficient and thus not feasible. Although there have been several recent studies on the maintenance of sequential patterns in an incremental manner, these works only consider the patterns extracted from time point-based data. Few research efforts have been elaborated on maintaining time interval-based sequential patterns, also called temporal patterns, where each datum persists for a period of time. In this paper, an efficient algorithm, Inc_TPMiner (Incremental Temporal Pattern Miner) is developed to incrementally discover temporal patterns from interval-based data. Moreover, the algorithm employs some optimization techniques to reduce the search space effectively. The experimental results on both synthetic and real datasets indicate that Inc_TPMiner significantly outperforms re-mining with static algorithms in execution time and possesses graceful scalability. Furthermore, we also apply Inc_TPMiner on a real dataset to show the practicability of incremental mining of temporal patterns.
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References
Agrawal R, Srikant R (1995) Mining Sequential Patterns, Proceedings of 11th International Conference on Data Engineering. (ICDE’95), pp 3–14
Allen J (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843
Chang L, Wang T, Yang D, Luan H, Tang S (2009) Efficient algorithms for incremental maintenance of closed sequential patterns in large databases. Data Knowl Eng 68(1):68–106
Chen J (2010) An up down directed acyclic graph approach for sequential pattern mining. IEEE Trans Knowl Data Eng 22(7):913–928
Chen Y, Guo J, Wang Y, Xiong Y, Zhu Y (2007) Incremental Mining of Sequential Patterns using Prefix Tree, The 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’07), pp 433–440
Chen Y, Jiang J, Peng W, Lee S (2010) An Efficient Algorithm for Mining Time Interval-based Patterns in Large Databases, 19th ACM International Conference on Information and Knowledge Management (CIKM’10), pp 49–58
Cheng H, Yan X, Han J (2004) IncSpan: Incremental Mining of Sequential Patterns in Large Database, Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04), pp 527–532
Hoppner F (2002) Finding informative rules in interval sequences. Intell Data Anal 6(3):237–255
Kam P, Fu W (2000) Discovering Temporal Patterns for Interval-based Events, International Conference on Data Warehousing and Knowledge Discovery (DaWaK’00), pp 317–326
Lin M, Lee S (2004) Incremental update on sequential patterns in large databases by implicit merging and efficient counting. Inf Syst 29(5):385–404
Lin M, Lee S (2005) Fast discovery of sequential patterns by memory indexing and database partitioning. J Inf Sci Eng 21(1):109–128
Mannila H, Toivonen H, Verkamo I (1997) Discovery of frequent episodes in event sequences. Data Min Knowl Discov 1(3):259–289
Masseglia F, Cathala F, Poncelet P (1998) The PSP Approach for Mining Sequential Patterns, European Conference on Principles of Data Mining and Knowledge Discovery (PKDD’01), pp 176–184
Masseglia F, Poncelet P, Teisseire M (2003) Incremental mining of sequential patterns in large databases. Data Knowl Eng 46(1):97–121
Morchen F, Ultsch A (2007) Efficient mining of understandable patterns from multivariate interval time series. Data Min Knowl Discov 15(2):181–215
Nguyen S, Sun X, Orlowska M (2005) Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database, The 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’05), pp 442–451
Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2005) Discovering frequent arrangements of temporal intervals, International Conference on Data Mining (ICDM’05), pp 354–361
Parthasarathy S, Zaki M, Ogihara M, Dwarkadas S (1999) Incremental and interactive sequence mining, Proceedings of the 8th International Conference on Information and Knowledge Management (CIKM’99), pp 251–258
Patel D, Hsu W, Lee M (2008) Mining Relationships Among Interval-based Events for Classification, Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (KDD’08), pp 393–404
Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal U, Hsum M (2004) Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans Knowl Data Eng 16(10):1424–1440
Rowley A, Shulman S (1998) Kawasaki syndrome. Clin Microbiol Rev 11(3):405–414
Srikant R, Agrawal R (1996) Mining Sequential patterns: Generalizations and Performance Improvements, Proceedings of 5th International Conference on Extended Database Technology (EDBT’96), pp 3–17
Tang H, Liao S, Sun S (2013) A prediction framework based on contextual data to support mobile personalized marketing. Decis Support Syst 56:234–246
Villafane R, Hua K, Tran D (2000) Knowledge discovery from series of interval events. J Intell Inf Syst 15:71–89
Winarko E, Roddick JF (2007) ARMADA-an algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl Eng 63(1):76–90
Wu S, Chen Y (2007) Mining nonambiguous temporal patterns for interval-based events. IEEE Trans Knowl Data Eng 19(6):742–758
Wu S, Chen Y (2009) Discovering hybrid temporal patterns from sequences consisting of point-and interval-based events. Data Knowl Eng 68(11):1309–1330
Zaki M (2001) SPADE: an efficient algorithm for mining frequent sequences. Mach Learn 42(1–2):31–60
Zhang L, Chen G, Brijs T, Zhang X (2008) Discovering during-temporal patterns (DTPs) in large temporal databases. Expert Syst Appl 34:1178–1189
Zhang M, Kao B, Cheung D, Yip C (2002) Efficient algorithms for incremental updates of frequent sequences, The 6th Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’02), pp 186–197
Acknowledgments
Yi-Cheng Chen was supported by the Ministry of Science and Technology, Project No. 103-2218-E-032 -003. Julia Tzu-Ya Weng was supported by the Ministry of Science and Technology under Project No. 103-2221-E-155-038.
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Hui, L., Chen, YC., Weng, J.TY. et al. Incremental mining of temporal patterns in interval-based database. Knowl Inf Syst 46, 423–448 (2016). https://doi.org/10.1007/s10115-015-0828-5
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DOI: https://doi.org/10.1007/s10115-015-0828-5