Incremental Mining of Shocking Association Patterns
Association rules are an important problem in data
mining. Massively increasing volume of data in real life databases
has motivated researchers to design novel and incremental algorithms
for association rules mining. In this paper, we propose an incremental
association rules mining algorithm that integrates shocking
interestingness criterion during the process of building the model. A
new interesting measure called shocking measure is introduced. One
of the main features of the proposed approach is to capture the user
background knowledge, which is monotonically augmented. The
incremental model that reflects the changing data and the user beliefs
is attractive in order to make the over all KDD process more
effective and efficient. We implemented the proposed approach and
experiment it with some public datasets and found the results quite
promising.
[1] Han, J. and Kamber, M.: Data Mining: Concepts and Techniques. San
Francisco, Morgan Kauffmann Publishers, (2001).
[2] Dunham M. H.: Data Mining: Introductory and Advanced Topics. 1st
Edition Pearson Education (Singapore) Pte. Ltd. (2003).
[3] Hand, D., Mannila, H. and Smyth, P.: Principles of Data Mining,
Prentice-Hall of India Private Limited, India, (2001).
[4] Kaur H., Wasan. S. K, Al-Hegami A. S., Bhatnagar, V.: A Unified
Approach for Discovery of Interesting Association Rules. To appear in
Proceedings of Industrial Conference on Data Mining (ICDM), 2006.
[5] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Adaptive
Constraint Pushing in Frequent Pattern Mining. In Proceedings of the
17th European Conference on PAKDD03. (2003).
[6] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: ExAMiner:
Optimized Level-wise Frequent pattern Mining with Monotone
Constraints. In Proceedings of the 3rd International Conference on Data
Mining (ICDM03). (2003).
[7] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Exante:
Anticipated Data Reduction in Constrained Pattern Mining. In
Proceedings of the 7th PAKDD03. (2003).
[8] Klemetinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.
I.: Finding Interesting Rules from Large Sets of Discovered Association
Rules. In Proceedings of the 3rd International Conference on Information
and Knowledge Management. Gaithersburg, Maryland. (1994).
[9] Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the Subjective
Interestingness of Association Rules. IEEE Intelligent Systems. (2000).
[10] Psaila, G.: Discovery of Association Rules Meta-Patterns. In
Proceedings of 2nd International Conference on Data Warehousing and
Knowledge Discovery (DAWAK99). (1999).
[11] Agrawal, R., Imielinski, T. and Swami, A.: Mining Association Rules
between Sets of Items in Large Databases, In ACM SIGMOD
Conference of Management of Data. Washington D.C., (1993).
[12] Hansel, G.: Sur le nombre des functions Boolenes Monotones den
variables. C.R. Acad. Sci. Paris, 262(20):1088-1090 (in French). (1966).
[13] Ganti, V., Gehrke, J. and Ramakrishnan, R.: DEMON: Mining and
Monitoring evolving data. In Proceeding of the 16th International
Conference on Data Engineering, San Diego, USA. (2000).
[14] Lee, S., and Cheung, D.: Maintenance of discovered association rules.
When to update? In Research Issues on Data Mining and Knowledge
Discovery. (1997).
[15] Zaki, M. and Hsiao, C.: Charm: An efficient algorithm for closed itemset
mining. In Proceeding of the 2nd SIAM International Conference on Data
Mining, Arlington, USA. (2002).
[16] Cheung, D. W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of
discovered Association Rules in Large Databases: An Incremental
Updating Technique, Proc. the International Conference On Data
Engineering, (1996) 106-114.
[17] Cheung, D. W., Ng, V.T., Tam, B.W.: Maintenance of Discovered
Knowledge: A case in Multi-level Association Rules, Proc. 2nd
International Conference on Knowledge Discovery and Data Mining,
(1996) 307-310.
[18] Cheung, D. W., Lee, S.D., Kao, B.: A general Incremental Technique for
Mining Discovered Association Rules, Proc. International Conference
on Database System for Advanced Applications, (1997) 185-194.
[19] Yafi, E., Alam, M.A., Biswas, R.: Development of Subjective Measures
of Interestingness: From Unexpectedness to Shocking, Proceedings of
World Academy of Science, Engineering and Technology Volume 26
December 2007 ISSN 1307-6884.
[1] Han, J. and Kamber, M.: Data Mining: Concepts and Techniques. San
Francisco, Morgan Kauffmann Publishers, (2001).
[2] Dunham M. H.: Data Mining: Introductory and Advanced Topics. 1st
Edition Pearson Education (Singapore) Pte. Ltd. (2003).
[3] Hand, D., Mannila, H. and Smyth, P.: Principles of Data Mining,
Prentice-Hall of India Private Limited, India, (2001).
[4] Kaur H., Wasan. S. K, Al-Hegami A. S., Bhatnagar, V.: A Unified
Approach for Discovery of Interesting Association Rules. To appear in
Proceedings of Industrial Conference on Data Mining (ICDM), 2006.
[5] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Adaptive
Constraint Pushing in Frequent Pattern Mining. In Proceedings of the
17th European Conference on PAKDD03. (2003).
[6] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: ExAMiner:
Optimized Level-wise Frequent pattern Mining with Monotone
Constraints. In Proceedings of the 3rd International Conference on Data
Mining (ICDM03). (2003).
[7] Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Exante:
Anticipated Data Reduction in Constrained Pattern Mining. In
Proceedings of the 7th PAKDD03. (2003).
[8] Klemetinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.
I.: Finding Interesting Rules from Large Sets of Discovered Association
Rules. In Proceedings of the 3rd International Conference on Information
and Knowledge Management. Gaithersburg, Maryland. (1994).
[9] Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the Subjective
Interestingness of Association Rules. IEEE Intelligent Systems. (2000).
[10] Psaila, G.: Discovery of Association Rules Meta-Patterns. In
Proceedings of 2nd International Conference on Data Warehousing and
Knowledge Discovery (DAWAK99). (1999).
[11] Agrawal, R., Imielinski, T. and Swami, A.: Mining Association Rules
between Sets of Items in Large Databases, In ACM SIGMOD
Conference of Management of Data. Washington D.C., (1993).
[12] Hansel, G.: Sur le nombre des functions Boolenes Monotones den
variables. C.R. Acad. Sci. Paris, 262(20):1088-1090 (in French). (1966).
[13] Ganti, V., Gehrke, J. and Ramakrishnan, R.: DEMON: Mining and
Monitoring evolving data. In Proceeding of the 16th International
Conference on Data Engineering, San Diego, USA. (2000).
[14] Lee, S., and Cheung, D.: Maintenance of discovered association rules.
When to update? In Research Issues on Data Mining and Knowledge
Discovery. (1997).
[15] Zaki, M. and Hsiao, C.: Charm: An efficient algorithm for closed itemset
mining. In Proceeding of the 2nd SIAM International Conference on Data
Mining, Arlington, USA. (2002).
[16] Cheung, D. W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of
discovered Association Rules in Large Databases: An Incremental
Updating Technique, Proc. the International Conference On Data
Engineering, (1996) 106-114.
[17] Cheung, D. W., Ng, V.T., Tam, B.W.: Maintenance of Discovered
Knowledge: A case in Multi-level Association Rules, Proc. 2nd
International Conference on Knowledge Discovery and Data Mining,
(1996) 307-310.
[18] Cheung, D. W., Lee, S.D., Kao, B.: A general Incremental Technique for
Mining Discovered Association Rules, Proc. International Conference
on Database System for Advanced Applications, (1997) 185-194.
[19] Yafi, E., Alam, M.A., Biswas, R.: Development of Subjective Measures
of Interestingness: From Unexpectedness to Shocking, Proceedings of
World Academy of Science, Engineering and Technology Volume 26
December 2007 ISSN 1307-6884.
@article{"International Journal of Information, Control and Computer Sciences:63330", author = "Eiad Yafi and Ahmed Sultan Al-Hegami and M. A. Alam and Ranjit Biswas", title = "Incremental Mining of Shocking Association Patterns", abstract = "Association rules are an important problem in data
mining. Massively increasing volume of data in real life databases
has motivated researchers to design novel and incremental algorithms
for association rules mining. In this paper, we propose an incremental
association rules mining algorithm that integrates shocking
interestingness criterion during the process of building the model. A
new interesting measure called shocking measure is introduced. One
of the main features of the proposed approach is to capture the user
background knowledge, which is monotonically augmented. The
incremental model that reflects the changing data and the user beliefs
is attractive in order to make the over all KDD process more
effective and efficient. We implemented the proposed approach and
experiment it with some public datasets and found the results quite
promising.", keywords = "Knowledge discovery in databases (KDD), Data
mining, Incremental Association rules, Domain knowledge,
Interestingness, Shocking rules (SHR).", volume = "3", number = "1", pages = "201-5", }
