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A greedy classification algorithm based on association rule

Thabtah, Fadi Abdeljaber and Cowling, Peter (2007) A greedy classification algorithm based on association rule. Applied Soft Computing, 7 (3). pp. 1102-1111. ISSN 1568-4946

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    Classification and association rule discovery are important data mining tasks. Using association rule discovery to construct classification systems, also known as associative classification, is a promising approach. In this paper, a new associative classification technique, Ranked Multilabel Rule (RMR) algorithm is introduced, which generates rules with multiple labels. Rules derived by current associative classification algorithms overlap in their training objects, resulting in many redundant and useless rules. However, the proposed algorithm resolves the overlapping between rules in the classifier by generating rules that does not share training objects during the training phase, resulting in a more accurate classifier. Results obtained from experimenting on 20 binary, multi-class and multi-label data sets show that the proposed technique is able to produce classifiers that contain rules associated with multiple classes. Furthermore, the results reveal that removing overlapping of training objects between the derived rules produces highly competitive classifiers if compared with those extracted by decision trees and other associative classification techniques, with respect to error rate

    Item Type: Article
    Additional Information: UoA 23 (Computer Science and Informatics) © 2006 Elsevier B.V.
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics
    Q Science > QA Mathematics > QA76 Computer software
    Schools: School of Computing and Engineering

    [1] R. Agrawal, R. Srikant, Fast algorithms for mining association rule, in:
    Proceedings of the 20th International Conference on Very Large Data
    Bases, 1994, pp. 487–499.
    [2] E. Baralis, P. Torino, A lazy approach to pruning classification rules, in:
    Proceedings of the 2002 IEEE International Conference on Data Mining,
    2002, p. 35.
    [3] J. Cendrowska, PRISM: an algorithm for inducing modular rules, Int. J.
    Man-Mach. Stud. 27 (4) (1987) 349–370.
    [4] W. Cohen, Fast effective rule induction, in: Proceedings of the 12th
    International Conference on Machine Learning, Morgan Kaufmann,
    CA, (1995), pp. 115–123.
    [5] P. Cowling, K. Chakhlevitch, Hyperheuristics for managing a large
    collection of low level heuristics to schedule personnel, in: Proceedings
    of 2003 IEEE Conference on Evolutionary Computation, Canberra,
    Australia, 8–12 December, (2003), pp. 1214–1221.
    [6] P. Cowling, G. Kendall, E. Soubeiga, A hyperheuristic approach to
    scheduling a sales summit, in: Proceedings of the Third International
    Conference of Practice and Theory of Automated Timetabling (PATAT
    2000), Constance, Germany, (2000), pp. 176–190.
    [7] U. Fayyad, K. Irani, Multi-interval discretisation of continues-valued
    attributes for classification learning, in: Proceeding of the IJCAI, 1993,
    [8] J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate
    generation, in: Proceedings of the 2000 ACM SIGMOD International
    Conference on Management of Data, Dallas, Texas, (2000), pp. 1–12.
    [9] W. Li, J. Han, J. Pei, CMAR: accurate and efficient classification based on
    multiple-class association rule, in: Proceedings of the ICDM’01, San Jose,
    CA, (2001), pp. 369–376.
    [10] B. Liu, W. Hsu, Y. Ma, Integrating classification and association
    rule mining, in: Proceeding of the KDD, New York, NY, (1998), pp.
    [11] C. Merz, P. Murphy, UCI Repository of Machine Learning Data-bases,
    Department of Information and Computer Science, University of California,
    Irvine, CA, 1996.
    [12] J. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann,
    San Mateo, CA, 1993.
    [13] J. Quinlan, R. Cameron-Jones, FOIL: a midterm report, in: Proceedings of
    the European Conference on Machine Learning, Vienna, Austria, (1993),
    pp. 3–20.
    [14] F. Thabtah, Rule preference effect in associative classification mining, J.
    Inform. Knowl. Manage. 5 (1) (2006) 1–7.
    [15] F. Thabtah, P. Cowling, Y. Peng, MCAR: multi-class classification based
    on association rule approach, in: Proceeding of the Third IEEE International
    Conference on Computer Systems and Applications, Cairo, Egypt,
    (2005), pp. 1–7.
    [16] F. Thabtah, Rules pruning in associative classification mining, in: Proceedings
    of the IBIMA Conference, 2005, Cairo, Egypt, pp. 7–15.
    [17] F. Thabtah, P. Cowling, Y. Peng, A new multi-class multi-label associative
    classification approach, in: Proceeding of the Fourth International Conference
    on Data Mining, Brighton, UK, (2004), pp. 217–224.
    [18] K. Wang, S. Zhou, Y. He, Growing decision tree on support-less association
    rules, in: Proceedings of the Sixth ACM SIGKDD International
    Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts,
    (2000), pp. 265–269.
    [19] I. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and
    Techniques with Java Implementations, Morgan Kaufmann, San Francisco,
    [20] X. Yin, J. Han, CPAR: classification based on predictive association
    rule, in: Proceedings of the SDM, San Francisco, CA, (2003), pp. 369–
    [21] M. Zaki, K. Gouda, Fast vertical mining using diffsets, in: Proceedings
    of the Ninth ACM SIGKDD International Conference on Knowledge
    Discovery and Data Mining, Washington, DC, (2003), pp. 326–
    [22] M. Zaki, S. Parthasarathy, M. Ogihara, W. Li, New algorithms for fast
    discovery of association rules, in: Proceedings of the Third KDD Conference,
    1997, pp. 283–286.
    [23] CBA:
    [24] WEKA: Data Mining Software in Java:

    Depositing User: Sara Taylor
    Date Deposited: 02 Jul 2007
    Last Modified: 28 Jul 2010 19:20


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