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Mining frequent arrangements of temporal intervals

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Abstract

The problem of discovering frequent arrangements of temporal intervals is studied. It is assumed that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine temporal arrangements of event intervals that appear frequently in the database. The motivation of this work is the observation that in practice most events are not instantaneous but occur over a period of time and different events may occur concurrently. Thus, there are many practical applications that require mining such temporal correlations between intervals including the linguistic analysis of annotated data from American Sign Language as well as network and biological data. Three efficient methods to find frequent arrangements of temporal intervals are described; the first two are tree-based and use breadth and depth first search to mine the set of frequent arrangements, whereas the third one is prefix-based. The above methods apply efficient pruning techniques that include a set of constraints that add user-controlled focus into the mining process. Moreover, based on the extracted patterns a standard method for mining association rules is employed that applies different interestingness measures to evaluate the significance of the discovered patterns and rules. The performance of the proposed algorithms is evaluated and compared with other approaches on real (American Sign Language annotations and network data) and large synthetic datasets.

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References

  1. Abraham T, Roddick JF (1999) Incremental Meta-Mining from Large Temporal Data Sets. In: ER ’98: proceedings of the workshops on data warehousing and data mining, pp 41–54

  2. Agrawal R et al (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD, pp 207–216

  3. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of VLDB, pp 487–499

  4. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of IEEE ICDE, pp 3–14

  5. Ale JM, Rossi GH (2000) An Approach to Discovering Temporal Association Rules. In: Proceedings of the SAC, pp 294–300

  6. Allen J, Ferguson G (1994) Actions and events in interval temporal logic. J Logic Comput

  7. Ayres J et al (2002) Sequential PAttern Mining using a Bitmap Representation. In: Proceedings of ACM SIGKDD, pp 429–435

  8. Baker-Shenk C (1983) A Micro-analysis of the nonmanual components of questions in American Sign Language. Doctoral Dissertation

  9. Bayardo R et al (1999) Constraint-based rule mining in large, dense databases. In: Proceedings of IEEE ICDE, pp 188–197

  10. Bayardo RJ (1998) Efficiently mining long patterns from databases. In: Proceedings of ACM SIGMOD, pp 85–93

  11. Brin S et al (1997) Beyond marketbaskets: generalizing association rules to correlations. In: ACM international conference on management of data (SIGMOD), pp 265–276

  12. Brin S et al (2004) Dynamic itemset counting and implication rules for market basket data. In: ACM international conference on management of data (SIGMOD), pp 255–264

  13. Casas-Garriga G (2005) Summarizing sequential data with closed partial orders. In: Proceedings of SDM

  14. Chen X, Petrounias I (1999) Mining temporal features in association rules. In: Proceedings of PKDD. Springer, London, pp 295–300

  15. Coulter GR (1979) American sign language typology. Doctoral dissertation

  16. Davey B, Priestley H (2002) Introduction to lattices and oder. Cambridge University Press, Cambridge

    Google Scholar 

  17. Winarko E, Roddick JF (2005) Discovering richer temporal association rules from interval-based data. In: Proceedings of DaWaK

  18. Garofalakis M et al (1999) SPIRIT: sequential pattern mining with regular expression constraints. In: Proceedings of VLDB, pp 223–234

  19. Giannotti F et al (2006) Efficient mining of temporally annotated sequences. In: SDM

  20. Han J et al (2000) FreeSpan: frequent pattern-projected sequential pattern mining. In: Proceedings of ACM SIGKDD, pp 355–359

  21. Han J et al (2000b) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD, pp 1–12

  22. Harms S et al (2002) Discovering sequential association rules with constraints and time lags in multiple sequences. In: International symposium on methodologies for intelligent systems (ISMIS), pp 432–442

  23. Hilderman R, Hamilton H (1999) Knowledge discovery and interestingness measures: a survey. Technical Report 99-04, Department of Computer Science, University of Regina

  24. Hilderman RJ, Hamilton HJ (2001) Evaluation of interestingness measures for ranking discovered knowledge. Lect Notes Comput Sci 2035: 247

    Article  Google Scholar 

  25. Hoeppner F (2001) Discovery of temporal patterns—learning rules about the qualitative behaviour of time series. In: Proceedings of PKDD, pp 192–203

  26. Hoeppner F, Klawonn F (2001) Finding informative rules in interval sequences. In: Advances in intelligent data analysis, proc. of the fourth international symposium, pp 123–132

  27. Hwang S-Y et al (2004) Discovery of temporal patterns from process instances. Comput Ind 53(3): 345–364

    Article  Google Scholar 

  28. Ji X et al (2007) Mining minimal distinguishing subsequence patterns with gap constraints. Knowl Inf Syst 11(3): 259–286

    Article  Google Scholar 

  29. Kam P, Fu AW (2000) Discovering temporal patterns for interval-based events. In: DaWaK, pp 317–326

  30. Kamber M, Shinghal R (1996) Evaluating the interestingness of characteristic rules. In: Proceedings of ACM SIGKDD, pp 263–266

  31. Laxman S et al (2007) Discovering frequent generalized episodes when events persist for different durations. IEEE Trans Knowl Data Eng 19(9): 1188–1201

    Article  Google Scholar 

  32. Leleu M et al (2003) GO-SPADE: mining sequential patterns over databases with consecutive repetitions. In: Proceedings of MLDM, pp 293–306

  33. Leung CK-S et al (2007) CanTree: a canonical-order tree for incremental frequent-pattern mining. Knowl Inf Syst 11(3): 287–311

    Article  Google Scholar 

  34. Liddell SK (1980) American Sign Language Syntax. The Hague, Mouton

    Google Scholar 

  35. Lin J-L (2002) Mining maximal frequent intervals. Technical Report, Department of Information Management, Yuan Ze University

  36. Lin J-L (2003) Mining maximal frequent intervals. In: Proceedings of SAC, pp 624–629

  37. Lu H et al (1998) Stock movement prediction and n-dimensional inter-transaction association rules. In: Proceedings of the ACM SIGMOD workshop on research issues in data mining and knowledge discovery, pp 12:1–12:7

  38. Luo C, Chung SM (2008) A scalable algorithm for mining maximal frequent sequences using a sample. Knowl Inf Syst 15(2): 149–179

    Article  Google Scholar 

  39. Mannila H, Toivonen H (1996) Discovering generalized episodes using minimal occurences. In: Proceedings of ACM SIGKDD, pp 146–151

  40. Mannila H et al (1995) Discovering frequent episodes in sequences. In: Proceedings of ACM SIGKDD, pp 210–215

  41. Moerchen F (2006) Algorithms for time series knowledge mining. In: Proceedings of ACM SIGKDD

  42. Mooney C, Roddick JF (2004) Mining relationships between interacting episodes. In: Proceedings of SDM

  43. Neidle C (2002a) SignStream: a database tool for research on visual-gestural language. J Sign Lang Linguist 4: 203–214

    Article  Google Scholar 

  44. Neidle C (2002b) Signstream annotation: conventions used for the American Sign Language Linguistic Research Project. American Sign Language Linguistic Research Project Report 11

  45. Neidle C (2003) Language across modalities: ASL focus and question constructions. Linguist Var Yearbook 2: 71–93

    Article  Google Scholar 

  46. Neidle C et al (2000) The Syntax of American Sign Language: functional categories and hierarchical structure

  47. Neidle C, Lee RG (2006) Syntactic agreement across language modalities. Studies on Agreement

  48. Neidle C et al (2001) SignStream: a tool for linguistic and computer vision research on visual-gestural language data. Behav Res Methods Instrum Comput 33: 311–320

    Google Scholar 

  49. Oezden B et al (1998) Cyclic association rules. In: Proceedings of IEEE ICDE, pp 412–421

  50. Omiecinski ER (2003) Alternative interest measures for mining associations in databases. IEEE Trans Knowl Data Eng 15(1): 39–79

    Article  MathSciNet  Google Scholar 

  51. Papapetrou P et al (2006) Discovering frequent poly-regions in DNA sequences. In: Proceedings of the IEEE ICDM workshop on data mining in Bioinformatics

  52. Papapetrou P et al (2005) Discovering frequent arrangements of temporal intervals. In: Proceedings of IEEE ICDM, pp 354–361

  53. Pasquier N et al (1999) Discovering frequent closed itemsets for association rules. In: Proceedings of ICDT, pp 398–416

  54. Pei J et al (2000) CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Proceedings of DMKD, pp 11–20

  55. Pei J et al (2001) PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of IEEE ICDE, pp 215–224

  56. Pei J et al (2002) Constraint-based sequential pattern mining in large databases. In: Proceedings of CIKM, pp 18–25

  57. Piatetsky-Shapiro G (1991) Discovery, analysis and presentation of strong rules. In: Knowledge Discovery in Databases, pp 229–248

  58. Seno M, Karypis G (2002) SLPMiner: an algorithm for finding frequent sequential patterns using length-decreasing support constraint. In: Proceedings of IEEE ICDM, pp 418–425

  59. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Proceedings of EDBT, pp 3–17

  60. Steinbach et al (2007) Generalizing the notion of confidence. Knowl Inf Syst 12(3): 279–299

    Article  Google Scholar 

  61. Tan P, Kumar V (2000) Interestingness measures for association patterns: a perspective. Technical Report TR00-036, Department of Computer Science, University of Minnesota

  62. Tan P et al (2002) Selecting the right interestingness measure for association patterns. In: Proceedings of ACM SIGKDD, pp 183–192

  63. Tsoukatos I, Gunopulos D (2001) Efficient mining of spatiotemporal patterns. In: Proceedings of the SSTD, pp 425–442

  64. Villafane R et al (2000) Knowledge discovery from series of interval events. Intell Inf Syst 15(1): 71–89

    Article  Google Scholar 

  65. Wang J, Han J (2004) BIDE: efficient mining of frequent closed sequences. In: Proceedings of IEEE ICDE, pp 79–90

  66. Webb GI (2006) Discovering significant rules. In: Proceedings of ACM SIGKDD

  67. Webb GI, Zhang S (2005) k-Optimal-rule-discovery. Data Mining Knowl Discov 10: 39–79

    Article  MathSciNet  Google Scholar 

  68. 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  70. Xin D et al (2006) Discovering interesting patterns through user’s interactive feedback. In: Proceedings of ACM SIGKDD

  71. Yan X et al (2003) CloSpan: mining closed sequential patterns in large databases. In: Proceedings of SDM

  72. Zaki M (2001) SPADE: An efficient algorithm for mining frequent sequences. Mach Learn 40: 31–60

    Article  Google Scholar 

  73. Zaki M, Hsiao C (2002) CHARM: an efficient algorithm for closed itemset mining. In: Proceedings of SIAM, pp 457–473

  74. Zaki MJ (2000) Sequence mining in categorical domains: incorporating constraints. In: CIKM, pp 422–429

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Authors and Affiliations

  1. Department of Computer Science, Boston University, Boston, MA, 02215, USA

    Panagiotis Papapetrou, George Kollios & Stan Sclaroff

  2. Department of Informatics and Telecommunications, University of Athens, Athens, Greece

    Dimitrios Gunopulos

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  1. Panagiotis Papapetrou
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  2. George Kollios
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  3. Stan Sclaroff
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  4. Dimitrios Gunopulos
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Correspondence to Panagiotis Papapetrou.

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Papapetrou, P., Kollios, G., Sclaroff, S. et al. Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21, 133–171 (2009). https://doi.org/10.1007/s10115-009-0196-0

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