Skip to main content

Advertisement

Springer Nature Link
Log in

A novel algorithm for mining closed temporal patterns from interval-based data

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Achar A, Laxman S, Sastry P (2012) A unified view of the apriori-based algorithms for frequent episode discovery. Knowl Inf Syst 31:223–250

    Article  Google Scholar 

  2. Agrawal R, Srikant R (1995) Mining Sequential Patterns. In: Proceedings of 11th international conference on data engineering (ICDE’95), pp 3–14

  3. Allen J (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843

    Article  MATH  Google Scholar 

  4. Chang L, Wang T, Yang D, Luan H (2008) SeqStream: Mining closed sequential patterns over stream sliding windows. In: The 8th international conference on data mining (ICDM’08), pp 83–92

  5. Chen Y, Jiang J, Peng W, Lee S (2010) An efficient algorithm for mining time interval-based patterns in large databases. In: The 19th ACM international conference on information and knowledge management (CIKM’10), pp 49–58

  6. Hoppner F (2002) Finding informative rules in interval sequences. Intel Data Anal 6(3):237–255

    MathSciNet  Google Scholar 

  7. Hsu F, Lee S, Lin B (1999) 2D C-tree spatial representation for iconic image. J Vis Lang Comput 10:147–164

    Article  Google Scholar 

  8. Huang K, Chang C, Tung J, Ho C (2006) COBRA: closed sequential pattern mining using bi-phase reduction approach. In: Proceedings of the 2006 international conference on data warehousing and knowledge discovery (DaWaK’06), pp 280–291

  9. Kam P, Fu W (2000) Discovering temporal patterns for interval-based events. In: Proceedings of the 2000 international conference on data warehousing and knowledge discovery (DaWaK’00), pp 317–326

  10. Lee S, Hsu F (1990) 2D C-string: a new spatial knowledge representation for image database systems. Pattern Recogn 23:1077–1087

    Article  Google Scholar 

  11. Li H, Huang H, Lee S (2011) Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits. Knowl Inf Syst 28:495–422

    Article  Google Scholar 

  12. Lin M, Lee S (2005) Fast discovery of sequential patterns by memory indexing and database partitioning. J Inf Sci Eng 21(1):109–128

    Google Scholar 

  13. Mannila H, Toivonen Verkamo I (1997) Discovery of frequent episodes in event sequences. Data Min Knowl Disc 1(3):259–289

    Article  Google Scholar 

  14. Masseglia F, Cathala F, Poncelet P (1998) The PSP approach for mining sequential patterns. In: European conference on principles of data mining and knowledge discovery (PKDD’01), pp 176–184

  15. Morchen F (2011) Temporal pattern mining in symbolic time point and time interval data. www.siam.org/meetings/sdm11/moerchen.pdf

  16. Morchen F, Ultsch A (2007) Efficient mining of understandable patterns from multivariate interval time series. Data Min Knowl Discov 15(2):181–215

    Article  MathSciNet  Google Scholar 

  17. Morchen F, Fradkin D (2010) Robust mining of time intervals with semi-interval partial order patterns. In: Proceedings of the SIAM international conference on data mining (SDM’10), pp 315–326

  18. Moskovitch R, Shahar Y (2009) Karmalego—fast time intervals mining. Technical Report 23, ISE-TECHREP Ben Gurion University

  19. Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2005) Discovering frequent arrangements of temporal intervals. In: The 5th international conference on data mining (ICDM’05), pp 354–361

  20. Patel D, Hsu W, Lee M (2008) Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data (SIGMOD’08), pp 393–404

  21. Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal D, Hsum M (2004) Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans Knowl Data Eng 16(10):1424–1440

    Google Scholar 

  22. Rodriguez-Gonzalez A, Martinez-Trinidad J, Carrasco-Ochoa J, Ruiz-Shulcloper J (2011) RP-Miner: a relaxed prune algorithm for frequent similar pattern mining. Knowl Inf Syst 27:451–471

    Article  Google Scholar 

  23. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Proceedings of 5th international conference on extended database technology (EDBT’96), pp 3–17

  24. Villafane R, Hua K, Tran D (2000) Knowledge discovery from series of interval events. J Intel Inf Syst 15:71–89

    Article  Google Scholar 

  25. Wang J, Han J (2004) BIDE: efficient mining of frequent closed sequences. In: Proceedings of the 20th international conference on data engineering (ICDE’04), pp 79–90

  26. Winarko E, Roddick J (2007) ARMADA—an algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl Eng 63(1):76–90

    Article  Google Scholar 

  27. Wu S, Chen Y (2007) Mining nonambiguous temporal patterns for interval-based events. IEEE Trans Knowl Data Eng 19(6):742–758

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. Yan X, Cheng H, Han J, Xin D (2003) CloSpan: mining closed sequential patterns in large datasets. In: Proceedings of 3rd SIAM international conference on data mining (SDM’3), pp 166–177

  30. Zaki M (2001) SPADE: an efficient algorithm for mining frequent sequences. Mach Learn 42(1–2):31–60

    Article  MATH  Google Scholar 

  31. Zaki M, Hsiao C (2002) CHARM: an Efficient algorithm for closed itemset mining. In: Proceedings of 2nd SIAM international conference on data mining (SDM’02), pp 457–478

  32. Zhang L, Chen G, Brijs T, Zhang X (2008) Discovering during-temporal patterns (DTPs) in large temporal databases. Expert Syst Appl 34:178–11

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan

    Yi-Cheng Chen

  2. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan

    Julia Tzu-Ya Weng

  3. Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan

    Julia Tzu-Ya Weng

  4. Department of Innovative Information and Technology, Tamkang University, New Taipei City, Taiwan

    Lin Hui

Authors
  1. Yi-Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julia Tzu-Ya Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Hui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi-Cheng Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, YC., Weng, J.TY. & Hui, L. A novel algorithm for mining closed temporal patterns from interval-based data. Knowl Inf Syst 46, 151–183 (2016). https://doi.org/10.1007/s10115-014-0815-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-014-0815-2

Keywords