Summary
In this chapter we address the problem of mining sequential classification rules. Unfortunately, while high support thresholds may yield an excessively small rule set, the solution set becomes rapidly huge for decreasing support thresholds. In this case, the extraction process becomes time consuming (or is unfeasible), and the generated model is too complex for human analysis.
We propose two compact forms to encode the knowledge available in a sequential classification rule set. These forms are based on the abstractions of general rule, specialistic rule, and complete compact rule. The compact forms are obtained by extending the concept of closed itemset and generator itemset to the context of sequential rules. Experimental results show that a significant compression ratio is achieved by means of both proposed forms.
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References
UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/mlrepository.html
R. Agrawal and R. Srikant. Fast algorithm for mining association rules. In VLDB’94, pages 487–499, 1994
R. Agrawal and R. Srikant. Mining sequential patterns. In IEEE ICDE’95, pages 3–14, 1995
R. Agrawal and R. Srikant. Mining sequential patterns: Generalizations and performance improvements. In EDBT 1996, pages 3–17
H. Ahonen-Myka. Discovery of frequent word sequences in text. In ESF Exploratory Workshop on Pattern Detection and Discovery 2002
E. Baralis and S. Chiusano. Essential classification rule sets. In ACM Transactions on Database Systems (TODS), vol. 29, no. 4, December 2004, pages 635–674
E. Baralis and P. Garza. A lazy approach to pruning classification rules. In IEEE ICDM’02, pages 35–42, 2002
Y. Bastide, R. Taouil, N. Pasquier, and L. Lakhal. Discovering frequent closed itemsets for association rules. In ICDT’99, pages 398–416, 1999
R.J. Bayardo. Efficiently mining long patterns from databases. In ACM SIGMOD’98, pages 85–93, 1998
B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In ACM KDD’98, pages 80–86, 1998
J.-F. Boulicaut, A. Bykowski, and C. Rigotti. Free-sets: A condensed representation of boolean data for the approximation of frequency queries. In Data Mining and Knowledge Discovery journal, vol. 7, pages 5–22, 2003
A. Bykowski and C. Rigotti. A condensed representation to find frequent patterns. In APODS 2001
T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’02), pp. 74–85, Springer, Berlin Heidelberg New York, 2002
B. Cremilleux and J.-F. Boulicaut. Simplest rules characterizing classes generated by delta-free sets. In Proceedings of the 22 Annual International Conference Knowledge Based Systems and Applied Artificial Intelligence (ES’02), pages 33–46, Springer, Berlin Heidelberg New York, December 2002
M. Garofalakis, R. Rastogi, and K. Shim. Spirit: Sequential pattern mining with regular expression constraints. In VLDB 1999, pages 223–234
M.J. Zaki. Sequence mining in categorical domains: Incorporating constraints. In CIKM 2004, pages 442–429
M. Leleu, C. Rigotti, J.-F. Boulicaut, and G. Euvrard. Constraint-based mining of sequential pattern over datasets with consecutive repetitions. In PKDD 1997, pages 303–314
W. Li, J. Han, and J. Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. In IEEE ICDM’01, pages 369–376, 2001
H. Mannila, H. Toivonen, and I. Verkamo. Discovery of frequent episodes in event sequence. In DMKD 1997, pages 259–289
S. Orlando, R. Perego, and C. Silvestri. A new algorithm for gap constrained sequence mining. In ACM SAC 2004
N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Closed set based discovery of small covers for association rules. In BDA 1999, pages 361–381
N. Pasquier, Y. Bastide, R. Taouil, G. Stumme, and L. Lakhal. Mining minimal non-redundant association rules using frequent closed itemsets. In CL 2000, pages 972–986
J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan mining sequential patterns efficiently by prefix projected pattern growth. In IEEE ICDE’01, pages 215–226, 2001
J. Pei, J. Han, and W. Wang. Mining sequential patterns with constraints in large databases. In CIKM 2002
R. She, F. Chen, K. Wang, M. Ester, J.L. Gardy, and F.S.L. Brinkman. Frequent-subsequence-based prediction of outer membrane proteins. In SIGKDD 2003, pages 436–445
P. Tzvetkov, X. Yan, and J. Han. Sequential pattern mining using bitmap representation. In SIGKDD 2002, pages 429–435
P. Tzvetkov, X. Yan, and J. Han. Tsp: Mining top-k closed sequential patterns. In IEEE ICDM 2003, pages 347–354
J. Wang and J. Han. Bide: Efficient mining of frequent closed sequences. In IEEE ICDE’04, pages 79–90, 2004
K. Wang, S. Zhou, and Y. He. Growing decision trees on support-less association rules. In KDD’00, Boston, MA, pages 265–269, 2000
X. Yan, J. Han, and R. Afshar. Clospan: Mining closed sequential patterns in large datasets. In SIAM 2003
Q. Yang, I.T.Y. Li, and K. Wang. Building association-rule based sequential classifiers for web-document prediction. In DMKD 2004, pages 253–273
M. Zaki. Generating non-redundant association rules. In KDD 2000, pages 34–43
M. Zaki and C.-J. Hsiao. Charm: An efficient algorithm for closed itemset mining. In SIAM 2002
M.J. Zaki. Spade: An efficient algorithm for mining frequent sequences. Machine Learning (42), pages 31–60, 2001
M. Zhang, B. Kao, D.W. Cheung, and K.Y. Yip. Mining periodic patterns with gap requirement from sequences. In ACM SIGMOD’05, pages 623–633, 2005
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Baralis, E., Chiusano, S., Dutto, R., Mantellini, L. (2008). Compact Representations of Sequential Classification Rules. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_1
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