Abstract
Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.
- Aggarwal, C. C. 2007. Data Streams: Models and Algorithms. Springer, New York. Google ScholarDigital Library
- Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB). J. B. Bocca, M. Jarke, and C. Zaniolo, Eds., Morgan Kaufmann, 487--499. Google ScholarDigital Library
- Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proceedings of the 11th International Conference on Data Engineering (ICDE'95). P. S. Yu and A. S. P. Chen, Eds., IEEE Computer Society Press, 3--14. Google ScholarDigital Library
- Agrawal, R. C., Aggarwal, C. C., and Prasad, V. V. V. 1999. A tree projection algorithm for generation of frequent itemsets. In High Performance Data Mining Workshop. ACM Press. Google ScholarDigital Library
- Aho, A.1990. Algorithms for Finding Patterns in Strings. Vol. A: Algorithms and Complexity. MIT Press, Cambridge, MA, 255--300. Google ScholarDigital Library
- Ahonen, H., Heinonen, O., Klemettinen, M., and Verkamo, A. I. 1997. Applying data mining techniques in text analysis. Tech. rep. C-1997-23, Department of Computer Science, University of Helsinki.Google Scholar
- Ahonen, H., Heinonen, O., Klemettinen, M., and Verkamo, A. I. 1998. Applying data mining techniques for descriptive phrase extraction in digital document collections. In Proceedings of the Advances in Digital Libraries Conference. IEEE Computer Society, 2. Google ScholarDigital Library
- Albert-Lorincz, H. and Boulicaut, J.-F. 2003a. A framework for frequent sequence mining under generalized regular expression constraints. In Proceedings of the 2<sup>nd</sup> International Workshop on Inductive Databases. KDID, J.-F. Boulicaut and S. Dzeroski, Eds., 2--16.Google Scholar
- Albert-Lorincz, H. and Boulicaut, J.-F. 2003b. Mining frequent sequential patterns under regular expressions: A highly adaptive strategy for pushing contraints. In Proceedings of the 3rd SIAM International Conference on Data Mining. D. Barbar'a and C. Kamath, Eds., SIAM.Google Scholar
- Allen, J. F. 1983. Maintaining knowledge about temporal intervals. Comm. ACM 26, 11,832--843. Google ScholarDigital Library
- Amir, A., Lewenstein, M., and Porat, E. 2000. Faster algorithms for string matching with k mismatches. In Proceedings of the 11<sup>th</sup> Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 794--803. Google ScholarDigital Library
- Antunes, C. and Oliveira, A. L. 2004. Sequential pattern mining with approximated constraints. In Proceedings of the International Conference on Applied Computing.Google Scholar
- Arslan, A. N. and Egecioglu, O. 1999. An efficient uniform-cost normalized edit distance algorithm. In Proceedings of the 6th Symposium on String Processing and Information Retrieval (SPIRE'99). IEEE Computer Society, 8--15. Google ScholarDigital Library
- Arslan, A. N. and Egecioglu, O. 2000. Efficient algorithms for normalized edit distance. J. Discr. Algor. 1, 1, 3--20.Google Scholar
- Ayres, J., Flannick, J., Gehrke, J., and Yiu, T. 2002. Sequential pattern mining using a bitmap representation. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 429--435. Google ScholarDigital Library
- Batu, T., Ergün, F., Kilian, J., Magen, A., Raskhodnikova, S., Rubinfeld, R., and Sami, R. 2003. A sublinear algorithm for weakly approximating edit distance. In Proceedings of the 35th ACM Symposium on Theory of Computing. ACM Press, 316--324. Google ScholarDigital Library
- Bayardo, R. J. and Agrawal, R. 1999. Mining the most interesting rules. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining. S. Chaudhuri and D. Madigan, Eds., ACM Press, 145--154. Google ScholarDigital Library
- Bentley, J. L. and Sedgewick, R. 1997. Fast algorithms for sorting and searching strings. In Proceedings of the 8th Annual ACM/SIAM Symposium on Discrete Algorithms. SIAM, 360--369. Google ScholarDigital Library
- Breslauer, D. and Gąsieniec, L. 1995. Efficient string matching on coded texts. In Proceedings of the 6th Annual Symposium on Combinatorial Pattern Matching. Z. Galil and E. Ukkonen, Eds., Springer, 27--40.Google Scholar
- Bunke, H. and Csirik, J. 1992. Edit distance of run-length coded strings. In Proceedings of the ACM/SIGAPP Symposium on Applied Computing. ACM Press, 137--143. Google ScholarDigital Library
- Cai, Y. D., Clutter, D., Pape, G., Han, J., Welge, M., and Auvil, L. 2004. Maids: Mining alarming incidents from data streams. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, 919--920. Google ScholarDigital Library
- Casas-Garriga, G. 2005. Summarizing sequential data with closed partial orders. In Proceedings of the 5th SIAM International Conference on Data Mining. H. Kargupta, J. Srivastava, and A. Chandrika Kamath, Eds., Vol. 119, 380--391.Google ScholarCross Ref
- Ceglar, A. and Roddick, J. F. 2006. Association mining. ACM Comput. Surv. 38, 2. Google ScholarDigital Library
- Ceglar, A., Roddick, J. F., and Calder, P. 2003. Guiding Knowledge Discovery Through Interactive Data Mining. Idea Group Publishers, Hershey, PA, 45--87. Google ScholarDigital Library
- Chakrabarti, S., Sarawagi, S., and Dom, B. 1998. Mining surprising patterns using temporal description length. In Proceedings of the 24th International Conference on Very Large Data Bases, (VLDB'98). A. Gupta, O. Shmueli, and J. Widom, Eds. Morgan Kaufmann, 606--617. Google ScholarDigital Library
- Chan, S., Kao, B., Yip, C. L., and Tang, M. 2002. Mining emerging substrings. Tech. rep. TR-2002-11, HKU CSIS.Google Scholar
- Cheng, H., Yan, X., and Han, J. 2004. Incspan: Incremental mining of sequential patterns in large database. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04). ACM Press, 527--532. Google ScholarDigital Library
- Chiu, D.-Y., Wu, Y.-H., and Chen, A. L. P. 2004. An efficient algorithm for mining frequent sequences by a new strategy without support counting. In Proceedings of the 20th International Conference on Data Engineering (ICDE'04). IEEE Computer Society, 375--386. Google ScholarDigital Library
- Cole, R. and Hariharan, R. 1998. Approximate string matching: A simpler faster algorithm. In Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 463--472. Google ScholarDigital Library
- Cong, S., Han, J., and Padua, D. A. 2005. Parallel mining of closed sequential patterns. In Proceedings of the 11<sup>th</sup> ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. R. Grossman, R. Bayardo, and K. P. Bennett, Eds., ACM, 562--567. Google ScholarDigital Library
- Cormode, G. and Muthukrishnan, S. 2002. The string edit distance matching problem with moves. In Proceedings of the 13th Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 667--676. Google ScholarDigital Library
- Demiriz, A. and Zaki, M. J. 2002. webSPADE: A parallel sequence mining algorithm to analyze the web log data. In Proceedings of the 2<sup>nd</sup> IEEE International Conference on Data Mining. Google ScholarDigital Library
- El-Sayed, M., Ruiz, C., and Rundensteiner, E. A. 2004. FS-miner: Efficient and incremental mining of frequent sequence patterns in web logs. In Proceedings of the 6<sup>th</sup> ACM International Workshop on Web Information and Data Management (WIDM'04). A. H. F. Laender, D. Lee, and M. Ronthaler, Eds., ACM, 128--135. Google ScholarDigital Library
- Fiot, C., Laurent, A., and Teisseire, M. 2007. From crispness to fuzziness: Three algorithms for soft sequential pattern mining. IEEE Trans. Fuzzy Syst. 15, 6, 1263--1277. Google ScholarDigital Library
- Fu, Y. and Han, J. 1995. Meta-Rule-Guided mining of association rules in relational databases. In Proceedings of the 1<sup>st</sup> International Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD'95). 39--46.Google Scholar
- Gaber, M. M., Zaslavsky, A., and Krishnaswamy, S. 2005. Mining data streams: A review. SIGMOD Rec. 34, 2, 18--26. Google ScholarDigital Library
- Garofalakis, M. N., Rastogi, R., and Shim, K. 1999. SPIRIT: Sequential pattern mining with regular expression constraints. In Proceedings of the 25th International Conference on Very Large Databases (VLDB'99). 223--234. Google ScholarDigital Library
- Giannella, C., Han, J., Pei, J., Yan, X., and Yu, P. S. 2003. Mining frequent patterns in data streams at multiple time granularities. In Next Generation Data Mining. H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha, Eds., 191--212.Google Scholar
- Guralnik, V. and Karypis, G. 2004. Parallel tree-projection-based sequence mining algorithms. Parallel Comput. 30, 4, 443--472. Google ScholarDigital Library
- Guralnik, V., Wijesekera, D., and Srivastava, J. 1998. Pattern directed mining of sequence data. In Proceedings of the 4<sup>th</sup> International Conference on Knowledge Discovery and Data Mining (KDD '98). R. Agrawal, P. E. Stolorz, and G. Piatetsky-Shapiro, Eds., AAAI Press, 51--57.Google Scholar
- Hall, P. A. V. and Dowling, G. R. 1980. Approximate string matching. ACM Comput. Surv. 12, 4, 381--402. Google ScholarDigital Library
- Han, J., Cheng, H., Xin, D., and Yan, X. 2007. Frequent pattern mining: Current status and future directions. Data Mining Knowl. Discov. 15, 1, 55--86. Google ScholarDigital Library
- Han, J., Koperski, K., and Stefanovic, N. 1997. GeoMiner: A system prototype for spatial data mining. In Proceedings of the ACM SIGMOD International Conference on the Management of Data (SIGMOD '97). J. Peckham, Ed., ACM Press, 553--556. Google ScholarDigital Library
- Han, J. and Pei, J. 2000. Mining frequent patterns by pattern growth: Methodology and implications. SIGKDD Explor. Newslett. 2, 2, 14--20. Google ScholarDigital Library
- Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., and Hsu, M.-C. 2000a. Freespan: Frequent pattern-projected sequential pattern mining. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 355--359. Google ScholarDigital Library
- Han, J., Pei, J., and Yin, Y. 2000b. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD '00). ACM, 1--12. Google ScholarDigital Library
- Hingston, P. 2002. Using finite state automata for sequence mining. In Proceedings of the 25<sup>th</sup> Australasian Conference on Computer Science (ACSC'02). Australian Computer Society, Inc., 105--110. Google ScholarDigital Library
- Hong, T. P., Lin, K. Y., and Wang, S. L. 2001. Mining fuzzy sequential patterns from multiple-item transactions. In Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference. Vol. 3., IEEE, 1317--1321.Google Scholar
- Hoppner, F. 2001. Discovery of temporal patterns. Learning rules about the qualitative behaviour of time series. In Proceedings of the 5<sup>th</sup> European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'01). 192-203. Google ScholarDigital Library
- Hoppner, F. and Klawonn, F. 2002. Finding informative rules in interval sequences. Intell. Data Anal. 6, 6, 237--255.Google ScholarCross Ref
- Hsu, C. M., Chen, C. Y., Liu, B. J., Huang, C. C., Laio, M. H., Lin, C. C., and Wu, T. L. 2007. Identification of hot regions in protein-protein interactions by sequential pattern mining. BMC Bioinf. 8, 5, 8.Google ScholarCross Ref
- Hu, Y. C., Chen, R. S., Tzeng, G. H., and Shieh, J. H. 2003. A fuzzy data mining algorithm for finding sequential patterns. Int. J. Uncert., Fuzziness Knowl. Based Syst. 11, 2, 173--194. Google ScholarDigital Library
- Hu, Y. C., Tzeng, G. H., and Chen, C. M. 2004. Deriving two-stage learning sequences from knowledge in fuzzy sequential pattern mining. Inf. Sci. 159, 1-2, 69--86. Google ScholarDigital Library
- Huang, K.-Y., Chang, C.-H., and Lin, K.-Z. 2004. PROWL: An efficient frequent continuity mining algorithm on event sequences. In Proceedings of the 6<sup>th</sup> International Conference on Data Warehousing and Knowledge Discovery (DaWaK'04). Y. Kambayashi and W. Wöß, Eds., Lecture Notes in Computer Science, vol. 3181, Springer, 351--360.Google Scholar
- Huang, K.-Y., Chang, C.-H., and Lin, K.-Z. 2005. ClosedPROWL: Efficient mining of closed frequent continuities by projected window list technology. In Proceedigns of the SIAM International Conference on Data Mining.Google Scholar
- Hyyro, H. 2003. A bit-vector algorithm for computing levenshtein and damerau edit distances. Nordic J. Comput. 10, 1, 29--39. Google ScholarDigital Library
- Joshi, M. V., Karypis, G., and Kumar, V. 1999. Universal formulation of sequential patterns. Tech. rep. 99-21, Department of Computer Science, University of Minnesota.Google Scholar
- Kam, P.-S. and Fu, A. W.-C. 2000. Discovering temporal patterns for interval-based events. In Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWaK '00). Y. Kambayashi, M. K. Mohania, and A. M. Tjoa, Eds., Lecture Notes in Computer Science, vol. 1874., Springer, 317--326. Google ScholarDigital Library
- Kum, H.-C., Chang, J. H., and Wang, W. 2007a. Benchmarking the effectiveness of sequential pattern mining methods. Data Knowl. Engin. 60, 1, 30--50. Google ScholarDigital Library
- Kum, H.-C., Chang, J. H., and Wang, W. 2007B. Intelligent sequential mining via alignment: Optimization techniques for very large databases. In Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. (PAKDD'07). Springer, 587--597. Google ScholarDigital Library
- Kum, H.-C., Pei, J., Wang, W., and Duncan, D. 2002. ApproxMAP: Approximate mining of consensus sequential patterns. In Mining Sequential Patterns from Large Data Sets, W. Wang and J. Yang, Eds. Vol. 28., Springer.Google Scholar
- Landau, G. M., Myers, E. W., and Schmidt, J. P. 1998. Incremental string comparison. SIAM J. Comput. 27, 2, 557--582. Google ScholarCross Ref
- Laur, P.-A., Symphor, J.-E., Nock, R., and Poncelet, P. 2005. Mining sequential patterns on data streams: A near-optimal statistical approach. In Proceedigns of the 2<sup>nd</sup> International Workshop on Knowledge Discovery from Data Streams.Google Scholar
- Lin, J., Keogh, E., Lonardi, S., and Chiu, B. 2003. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. ACM Press, 2--11. Google ScholarDigital Library
- Luo, C. and Chung, S. M. 2004. A scalable algorithm for mining maximal frequent sequences using sampling. In Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI '04). IEEE Computer Society, 156--165. Google ScholarDigital Library
- Mabroukeh, N. R. and Ezeife, C. I. 2010. A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43, 1, 3. Google ScholarDigital Library
- Mannila, H. and Toivonen, H. 1996. Discovering generalized episodes using minimal occurrences. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD '96). AAAI Press, 146--151.Google Scholar
- Mannila, H., Toivonen, H., and Verkamo, A. I. 1995. Discovering frequent episodes in sequences. In Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (KDD '95). U. M. Fayyad and R. Uthurusamy, Eds., AAAI Press, 210--215.Google Scholar
- Mannila, H., Toivonen, H., and Verkamo, A. I. 1997. Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1, 3, 259--289. Google ScholarDigital Library
- Marascu, A. and Masseglia, F. 2005. Mining sequential patterns from temporal streaming data. In Proceedings of the 1<sup>st</sup> ECML/PKDD Workshop on Mining Spatio-Temporal Data (MSTD'05), held in conjunction with the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'05).Google Scholar
- Masseglia, F., Cathala, F., and Poncelet, P. 1998. The PSP approach for mining sequential patterns. In Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'98). Lecture Notes in Artificial Intelligence, vol. 1510., Springer, 176--184. Google ScholarDigital Library
- Masseglia, F., Poncelet, P., and Teisseire, M. 2000. Incremental mining of sequential patterns in large databases. Tech. rep., LIRMM.Google Scholar
- Mooney, C. H. and Roddick, J. F. 2006. Marking time in sequence mining. In Proceedings of the Australasian Conference on Data Mining and Analystics (AusDM '06). P. Christen, P. Kennedy, J. Li, S. Simoff, and G. Williams, Eds., Vol. 61. Google ScholarDigital Library
- Navarro, G. 2001. A guided tour to approximate string matching. ACM Comput. Surv. 33, 1, 31--88. Google ScholarDigital Library
- Ng, R. T., Lakshmanan, L. V. S., Han, J., and Pang, A. 1998. Exploratory mining and pruning optimizations of constrained associations rules. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'98). ACM, 13--24. Google ScholarDigital Library
- Nguyen, S. N., Sun, X., and Orlowska, M. E. 2005. Improvements of incspan: Incremental mining of sequential patterns in large database. In Proceedings of the 9th Pacific-Asia Conference (PAKDD'05). T. B. Ho, D. Cheung, and H. Liu, Eds., Vol. 3518., Springer, 442--451. Google ScholarDigital Library
- Oommen, B. J. and Loke, R. K. S. 1995. Pattern recognition of strings with substitutions, insertions, deletions and generalized transpositions. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 2. 1154--1159.Google Scholar
- Oommen, B. J. and Zhang, K. 1996. The Normalized String Editing problem revisited. IEEE Trans. Pattern Anal. Mach. Intell. 18, 6, 669--672. Google ScholarDigital Library
- Orlando, S., Perego, R., and Silvestri, C. 2004. A New Algorithm for gap constrained sequence mining. In Proceedings of the ACM Symposium on Applied Computing (SAC). ACM Press, 540--547. Google ScholarDigital Library
- Ouh, J. Z., Wu, P. H., and Chen, M. S. 2001. Experimental results on a constrained based sequential pattern mining for telecommunication alarm data. In Proceedings of the 2nd International Conference on Web Information Systems Engineering (WISE'01). IEEE Computer Society, 186--193.Google Scholar
- Padmanabhan, B. and Tuzhilin, A. 1996. Pattern discovery in temporal databases: A temporal logic approach. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. E. Simoudis, J. Han, and U. Fayyad, Eds., AAAI Press, 351--354.Google Scholar
- Pan, F., Cong, G., Tung, A. K. H., Yang, J., and Zaki, M. J. 2003. Carpenter: finding closed patterns in long biological datasets. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '03). ACM Press, 637--642. Google ScholarDigital Library
- Parthasarathy, S., Zaki, M. J., Ogihara, M., and Dwarkadas, S. 1999. Incremental and interactive sequence mining. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM). ACM, 251--258. Google ScholarDigital Library
- Pei, J., Han, J., and Lakshmanan, L. V. S. 2001a. Mining frequent itemsets with convertible constraints. In Proceedings of the 17th International Conference on Data Engineering. IEEE Computer Society, 433--442. Google ScholarDigital Library
- Pei, J., Han, J., and Mao, R. 2000a. CLOSET: An efficient algorithm for mining frequent closed itemsets. In Proceedings of the ACM SIGMOD International Workshop on Data Mining. ACM Press, 21--30.Google Scholar
- Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. 2001b. PrefixSpan mining sequential patterns efficiently by prefix projected pattern growth. In Proceedings of the International Conference of Data Engineering (ICDE'01). 215--226. Google ScholarDigital Library
- Pei, J., Han, J., Mortazavi-Asl, B., and Zhu, H. 2000b. Mining access patterns efficiently from web logs. In Proceedings of the 4th Pacific-Asia Conference (PAKDD'00). Lecture Notes in Computer Science, vol. 1805., Springer, 396--407. Google ScholarDigital Library
- Pei, J., Han, J., and Wang, W. 2002. Mining sequential patterns with constraints in large databases. In Proceedings of the 11th International Conference on Information and Knowledge Management. ACM Press, 18--25. Google ScholarDigital Library
- Pei, J., Han, J., and Wang, W. 2007. Constraint-Based sequential pattern mining: The pattern-growth methods. J. Intell. Inf. Syst. 28, 2, 133--160. Google ScholarDigital Library
- Pei, J., Liu, J., Wang, H., Wang, K., Yu, P. S., and Wang, J. 2005. Efficiently mining frequent closed partial orders. In Proceedings of the 5th IEEE International Conference on Data Mining. IEEE, 753--756. Google ScholarDigital Library
- Pei, J., Wang, H., Liu, J., Wang, K., Wang, J., and Yu, P. S. 2006. Discovering frequent closed partial orders from strings. IEEE Trans. Knowl. Data Engin. 18, 11, 1467--1481. Google ScholarDigital Library
- Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., and Dayal, U. 2001. Multi-Dimensional sequential pattern mining. In Proceedings of the 10th International Conference on Information and Knowledge Management. ACM Press, 81--88. Google ScholarDigital Library
- Rajman, M. and Besanç, on, R. 1998. Text mining -- knowledge extraction from unstructured textual data. In Proceedings of the 6th Conference of International Federation of Classification Societies (IFCS'98).Google ScholarCross Ref
- Sankoff, D. and Kruskal, J. B. 1999. Time Warps, String Edits, and Macromolecules/The Theory and Practice of Sequence Comparison. David Hume Series., Center for the Study of Language and Information, Stanford, CA.Google Scholar
- Savary, L. and Zeitouni, K. 2005. Indexed bit map (ibm) for mining frequent sequences. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'05). A. Jorge, L. Torgo, P. Brazdil, R. Camacho, and J. A. Gama, Eds., Lecture Notes in Computer Science, vol. 3721., Springer, 659--666.Google Scholar
- Seno, M. and Karypis, G. 2001. LPMiner: An algorithm for finding frequent itemsets using length-decreasing support constraint. In Proceedigns of the 1st IEEE Conference on Data Mining. Google ScholarDigital Library
- Seno, M. and Karypis, G. 2002. SLPMiner: An algorithm for finding frequent sequential patterns using length-decreasing support. Tech. rep. 02-023, University of Minnesota.Google Scholar
- Seno, M. and Karypis, G. 2005. Finding frequent patterns using length-decreasing support constraints. IEEE Trans. Knowl. Data Engin. 10, 3, 197--228.Google Scholar
- Srikant, R. and Agrawal, R. 1996. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the 5th International Conference on Extending Database Technology (EDBT'96), P. M. G. Apers, M. Bouzeghoub, and G. Gardarin, Eds., Lecture Notes in Computer Science, vol. 1057. Springer, 3--17. Google ScholarDigital Library
- Srivastava, J., Cooley, R., Deshpande, M., and Tan, P.-N. 2000. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explor. 1, 2, 12--23. Google ScholarDigital Library
- Sun, X., Orlowska, M. E., and Zhou, X. 2003. Finding event-oriented patterns in long temporal sequences. In Proceedings of the 7th Pacific-Asia Conference (PAKDD'03), K.-Y. Whang, J. J. and, K. S. and, and J. Srivastava, Eds., Lecture Notes in Computer Science, vol. 2637., Springer, 15--26. Google ScholarDigital Library
- Teng, W.-G., Chen, M.-S., and Yu, P. S. 2003. A regression-based temporal pattern mining scheme for data streams. In Proceedings of the 29th International Conference on Very Large Data Bases (VLBD '03), J. C. Freytag, P. C. Lockemann, S. Abiteboul, M. J. Carey, P. G. Selinger, and A. Heuer, Eds., Morgan Kaufmann, 93--104. Google ScholarDigital Library
- Tichy, W. F. 1984. The string-to-string correction problem with block moves. ACM Trans. Comput. Syst. 2, 4, 309--321. Google ScholarDigital Library
- Toivonen, H. 1996. Discovery of frequent patterns in large data collections. Tech. rep. a-1996-5, Department of Computer Science, University of Helsinki.Google Scholar
- Tumasonis, R. and Dzemyda, G. 2004. The probabilistic algorithm for mining frequent sequences. In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS).Google Scholar
- Wagner, R. A. and Fischer, M. J. 1974. The string-to-string correction problem. J. ACM 21, 1, 168--173. Google ScholarDigital Library
- Wang, J. and Han, J. 2004. Bide: Efficient mining of frequent closed sequences. In Proceedings of the International Conference on Data Engineering (ICDE'04). Google ScholarDigital Library
- Wang, J., Han, J., and Pei, J. 2003. CLOSET+: Searching for the best strategies for mining frequent closed itemsets. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. L. Getoor, T. E. Senator, P. Domingos, and C. Faloutsos, Eds., ACM Press, 236--245. Google ScholarDigital Library
- Wang, K. 1997. Discovering patterns from large and dynamic sequential data. J. Intell. Inf. Syst. 9, 1, 33--56. Google ScholarDigital Library
- Wang, K. and Tan, J. 1996. Incremental discovery of sequential patterns. In Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery.Google Scholar
- Wang, K., Xu, Y., and Yu, J. X. 2004. Scalable sequential pattern mining for biological sequences. In Proceedings of the 13th ACM International Conference on Information and Knowledge Management (CIKM'04). ACM, 178--187. Google ScholarDigital Library
- Wu, P. H., Peng, W. C., and Chen, M. S. 2001. Mining sequential alarm patterns in a telecommunication database. In Databases in Telecommunications II, W. Jonker, Ed., Lecture Notes in Computer Science, vol. 2209., Springer, 37--51. Google ScholarDigital Library
- Yan, X., Han, J., and Afshar, R. 2003. CloSpan: Mining closed sequential patterns in large datasets. In Proceedings of the International Conference on Data Mining (SDM'03).Google Scholar
- Yang, J., Wang, W., Yu, P. S., and Han, J. 2002. Mining long sequential patterns in a noisy environment. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'02). Google ScholarDigital Library
- Yang, Z. and Kitsuregawa, M. 2005. LAPIN-SPAM: An improved algorithm for mining sequential pattern. In Proceedings of the 21st International Conference on Data Engineering Workshops (ICDEW'05). IEEE Computer Society, 1222. Google ScholarDigital Library
- Yang, Z., Wang, Y., and Kitsuregawa, M. 2005. LAPIN: Effective sequential pattern mining algorithms by last position induction. In Proceedings of the 21st International Conference on Data Engineering (ICDE‘05).Google Scholar
- Yu, C.-C. and Chen, Y.-L. 2005. Mining sequential patterns from multidimensional sequence data. IEEE Trans. Knowl. Data Engin. 17, 1, 136--140. Google ScholarDigital Library
- Zaki, M., Lesh, N., and Ogihara, M. 1998. Planmine: Sequence mining for plan failures. In Proceedings of the 4<sup>th</sup> International Conference on Knowledge Discovery and Data Mining (KDD'98), R. Agrawal, P. Stolorz, and G. Piatetsky-Shapiro, Eds., ACM Press, 369--373.Google Scholar
- Zaki, M. J. 1998. Efficient enumeration of frequent sequences. In Proceedings of the 7th International Conference on Information and Knowledge Management. ACM Press, 68--75. Google ScholarDigital Library
- Zaki, M. J. 2000. Sequence mining in categorical domains: Incorporating constraints. In Proceedings of the 9th International Conference on Information and Knowledge Management (CIKM '00), A. Agah, J. Callan, and E. Rundensteiner, Eds., ACM Press, 422--429. Google ScholarDigital Library
- Zaki, M. J. 2001a. Parallel sequence mining on shared-memory machines. J. Parallel Distrib. Comput. 61, 3, 401--426. Google ScholarDigital Library
- Zaki, M. J. 2001b. SPADE: An efficient algorithm for mining frequent sequences. Mach. Learn. 42, 1/2, 31--60.Google Scholar
- Zaki, M. J. and Hsiao, C.-J. 2002. CHARM: An efficient algorithm for closed itemset mining. In Proceedings of the 2nd SIAM International Conference on Data Mining (SDM'02). R. L. Grossman, J. Han, V. Kumar, H. Mannila, and R. Motwani, Eds., SIAM, 457--473.Google Scholar
- Zhang, M., Kao, B., Cheung, D. W.-L., and Yip, C. L. 2002. Efficient algorithms for incremental update of frequent sequences. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 186--197. Google ScholarDigital Library
- Zhang, M., Kao, B., Yip, C., and Cheung, D. 2001. A GSP-based efficient algorithm for mining frequent sequences. In Proceedings of the International Conference on Artificial Intelligence (ICAI'01).Google Scholar
- Zhao, Q. and Bhowmick, S. S. 2003. Sequential pattern mining: A survey. Tech. rep., Nanyang Technological University, Singapore.Google Scholar
- Zheng, Q., Xu, K., Ma, S., and Lv, W. 2002. The algorithms of updating sequential patterns. In Proceedings of the 5th International Workshop on High Performance Data Mining,in conjunction with the 2nd SIAM Conference on Data Mining.Google Scholar
Index Terms
- Sequential pattern mining -- approaches and algorithms
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