Search:
Computing and Library Services - delivering an inspiring information environment

A review of associative classification mining

Thabtah, Fadi Abdeljaber (2007) A review of associative classification mining. Knowledge Engineering Review, 22 (1). pp. 37-65. ISSN 0269-8889

[img]
Preview
PDF
ThabtahReview.pdf

Download (339kB) | Preview

Abstract

Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper.

▼ Jump to Download Statistics
Item Type: Article
Additional Information: © 2007, Cambridge University Press
Uncontrolled Keywords: associative classification mining data association rule discovery technique classifiers CBA CPAR CMAR MCAR MMAC
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
References:

Agrawal, R. & Srikant, R. 1994 Fast algorithms for mining association rule. In Proceedings of the 20th International
Conference on Very Large Data Bases, Morgan Kaufmann, Santiago, Chile, pp. 487–499.
Agrawal, R., Amielinski, T. & Swami, A. 1993 Mining association rule between sets of items in large
databases. In Buneman, P. & Jajodia, S. (eds.), Proceedings of the ACM SIGMOD International Conference
on Management of Data, Washington, DC, pp. 207–216.
Ali, K., Manganaris, S. & Srikant, R. 1997 Partial classification using association rules. In Heckerman, D.,
Mannila, H., Pregibon, D. & Uthurusamy, R. (eds.), Proceedings of the 3rd International Conference on
Knowledge Discovery and Data Mining, Newport Beach, CA, pp. 115–118.
Antonie, M. & Zaïane, O. 2004 An associative classifier based on positive and negative rules. In Proceedings
of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. Paris,
France: ACM Press, pp. 64–69.
Antonie, M., Zaïane, O. & Coman, A. 2003 Associative classifiers for medical images. Mining Multimedia
and Complex Data (Lecture Notes in Artificial Intelligence, 2797). Berlin: Springer, pp. 68–83.
Baralis, E. & Torino, P. 2002 A lazy approach to pruning classification rules. Proceedings of the 2002 IEEE
International Conference on Data Mining (ICDM’02), Maebashi City, Japan, p. 35.
Baralis, E., Chiusano, S. & Graza, P. 2004 On support thresholds in associative classification. In Proceedings
of the 2004 ACM Symposium on Applied Computing. Nicosia, Cyprus: ACM Press, pp. 553–558.
Boutell, M., Shen, X., Luo, J. & Brown, C. 2003 Multi-label semantic scene classification. Technical Report
813, Department of Computer Science, University of Rochester, NY and Electronic Imaging Products
R & D, Eastern Kodak Company.
Cheung, D. W., Ng, V. T. & Tam, B. W. 1996 Maintenance of discovered knowledge: A case in multi-level
association rules. In Proceedings of the International Conference on Knowledge Discovery and Data Mining,
Portland, OR: AAAI Press, pp. 307–310.
Clare, A. & King, R. 2001 Knowledge discovery in multi-label phenotype data. In De Raedt, L. & Siebes, A.
(eds.), Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in
Databases (PKDD’01) (Lecture Notes in Artificial Intelligence, 2168). Berlin: Springer, pp. 42–53.
Clark, P. & Boswell, R. 1991 Rule induction with CN2: Some recent improvements. In Proceedings of the 5th
European Working Session on Learning. Berlin, Germany: Springer Verlag, pp. 151–163.
Cohen, W. 1995 Fast effective rule induction. In Proceedings of the 12th International Conference on Machine
Learning, Morgan Kaufmann, CA, pp. 115–123.
Dong, G., Zhang, X., Wong, L. & Li, J. 1999 CAEP: Classification by aggregating emerging patterns.
In Proceedings of the 2nd Imitational Conference on Discovery Science. Tokyo, Japan: Springer Verlag,
pp. 30–42.
Duda, R. & Hart, P. 1973 Pattern Classification and Scene Analysis. New York: Wiley.
Fayyad, U., Piatetsky-Shapiro, G., Smith, G. & Uthurusamy, R. 1998 Advances in Knowledge Discovery and
Data Mining. Menlo Park, CA: AAAI Press.
Freitas, A. 2000 Understanding the crucial difference between classification and association rule discovery.
ACM SIGKDD Explorations Newsletter 2, 65–69.
Furnkranz, J. & Widmer, G. 1994 Incremental reduced error pruning. In Proceedings of the 11th International
Machine Learning Conference, New Brunswick, NJ, pp. 70–75.
Gehrke, J., Ramakrishnan, R. & Ganti, V. 1998 RainForest: A Framework for fast decision tree construction
of large datasets. In Proceedings of the International Conference on very Large Data Bases, New York, NY,
pp. 416–427.
Han, J., Pei, J. & Yin, Y. 2000 Mining frequent patterns without candidate generation. In Proceedings
of the 2000 ACM SIGMOD International Conference on Management of Data. Dallas, TX: ACM Press,
pp. 1–12.
Hu, H. & Li, J. 2005 Using association rules to make rule-based classifiers robust. In Proceedings of the 16th
Australasian Database Conference, Newcastle, Australia, pp. 47–54.
Li, W. 2001 Classification based on multiple association rules. MSc thesis, Simon Fraser University, BC,
Canada, April 2001.
Li, W., Han, J. & Pei, J. 2001 CMAR: Accurate and efficient classification based on multiple-class
association rule. In Proceedings of the International Conference on Data Mining (ICDM’01), San Jose,
CA, pp. 369–376.
Lim, T., Loh, W. & Shih, Y. 2000 A comparison of prediction accuracy, complexity and training time of
thirty-three old and new classification algorithms. Machine Learning 40, 203–228.
Liu, B., Hsu, W. & Ma, Y. 1998 Integrating classification and association rule mining. In Proceedings of
the International Conference on Knowledge Discovery and Data Mining. New York, NY: AAAI Press,
pp. 80–86.
Liu, B., Hsu, W. & Ma, Y. 1999 Mining association rules with multiple minimum supports. In Proceedings of
the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego,
CA: ACM Press, pp. 337–341.
Liu, B., Ma, Y. & Wong, C.-K. 2000 Improving an association rule based classifier. In Proceedings of the 4th
European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, France, pp. 504–509.
Liu, B., Ma, Y., & Wong, C.-K. 2001 Classification using association rules: Weakness and enhancements. In
Vipin Kumar, et al. (eds), Data Mining for Scientific Applications, 2001.
Meretakis, D. & Wüthrich, B. 1999 Extending naïve Bayes classifiers using long itemsets. In Proceedings of
the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego,
CA: ACM Press, pp. 165–174.
Merz, C. & Murphy, P. 1996 UCI repository of machine learning databases. Irvine, CA: University of
California, Department of Information and Computer Science.
Provost, F., Fawcett, T. & Kohavi, R. 1997 The case against accuracy estimation for comparing induction
algorithms. In Proceedings of the 15th International Conference on Machine Learning, Madison, WI,
pp. 445–453.
Quinlan, J. 1987 Simplifying decision trees. International Journal of Man–Machine Studies 27, 221–248.
Quinlan, J. 1998 Data mining tools See5 and C5.0. Technical Report, RuleQuest Research.
Quinlan, J. 1993 C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
Quinlan, J. & Cameron-Jones, R. 1993 FOIL: A midterm report. In Proceedings of the European Conference
on Machine Learning. Vienna, Austria: Springer Verlag, pp. 3–20.
Savasere, A., Omiecinski, E. & Navathe, S. 1995 An efficient algorithm for mining association rules
in large databases. In Proceedings of the 21st conference on Very Large Databases (VLDB’95), Zurich,
Switzerland, pp. 432–444.
Schapire, R. & Singer, Y. 2000 BoosTexter: A boosting-based system for text categorization. Machine
Learning 39(2/3), 135–168.
Snedecor, W. & Cochran, W. 1989 Statistical Methods, 8th edn. Iowa City, IA: Iowa State University Press.
Thabtah F. 2006 Pruning techniques in associative classification: Survey and comparison. Journal of Digital
Information Management 4, 202–205.
Thabtah, F., Cowling, P. & Peng, Y. 2004 MMAC: A new multi-class, multi-label associative classification
approach. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04), Brighton,
UK, pp. 217–224.
Thabtah, F., Cowling, P. & Peng, Y. 2005 MCAR: Multi-class classification based on association rule
approach. In Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications,
Cairo, Egypt, pp. 1–7.
Topor, R. & Shen, H. 2001 Construct robust rule sets for classification. In Proceedings of the 8th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta,
Canada: ACM Press, pp. 564–569.
Tsai, P., Lee, C. & Chen, A. 1999 An efficient approach for incremental association rule mining. In
Proceedings of the 3rd Pacific–Asia Conference on Methodologies for Knowledge Discovery and Data
Mining. London, UK: Springer Verlag, pp. 74–83.
Valtchev, P., Missaoui, R., Godin, R. & Meridji, M. 2002 A framework for incremental generation of
frequent closed itemsets using galois (Concept) lattice theory. Journal of Experimental and Theoretical
Artificial Intelligence (JETAI), Special Issue on Concept Lattice based Theory, Methods and Tools for
Knowledge Discovery in Databases, 14, 115–142.
Van Rijsbergan, C. 1979 Information Retrieval, 2nd edn. London: Buttersmiths.
Wang, K., Zhou, S. & He, Y. 2000 Growing decision tree on support-less association rules. In Proceedings of
the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, MA:
ACM Press, pp. 265–269.
Wang, K., He, Y. & Cheung, D. 2001 Mining confidence rules without support requirements. In Proceedings
of the 10th International Conference on Information and Knowledge Management. Atlanta, GA: ACM
Press, pp. 89–96.
Weka, 2000 Data mining software in Java. http://www.cs.waikato.ac.nz/ml/weka.
Witten, I. & Frank, E. 2000 Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations.
San Francisco, CA: Morgan Kaufmann.
Xu, X., Han, G. & Min, H. 2004 A novel algorithm for associative classification of images blocks. In
Proceedings of the 4th IEEE International Conference on Computer and Information Technology, Lian,
Shiguo, China, pp. 46–51.
Yang, Y., Slattery, S. & Ghani, R. 2002 A study of approaches to hypertext categorization. Journal of
Intelligent Information Systems 18, 149–241.
Yin, X. & Han, J. 2003 CPAR: Classification based on predictive association rule. In Proceedings of the
SIAM International Conference on Data Mining. San Francisco, CA: SIAM Press, pp. 369–376.
Zaïane, O. & Antonie, A. 2002 Classifying text documents by associating terms with text categories. In
Proceedings of the 13th Australasian Database Conference (ADC’02), Melbourne, Australia, pp. 215–222.
Zaki, M. & Gouda, K. 2003 Fast vertical mining using diffsets. In Proceedings of the 9th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining. Washington, DC: ACM Press,
pp. 326–335.
Zaki, M., Parthasarathy, S., Ogihara, M. & Li, W. 1997 New algorithms for fast discovery of association
rules. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. Menlo
Park, CA: AAAI Press, pp. 283–286.
Zhou, Z. & Ezeife, C. 2001 A low-scan incremental association rule maintenance method based on the
Apriori property. In Proceedings of the 14th Biennial Conference of the Canadian Society on Computational
Studies of Intelligence: Advances in Artificial Intelligence. London, UK: Springer-Verlag, pp. 26–35.

Depositing User: Sara Taylor
Date Deposited: 02 Jul 2007
Last Modified: 28 Jul 2010 18:20
URI: http://eprints.hud.ac.uk/id/eprint/269

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©