Semi-Automatic Method to Assist Expert for Association Rules Validation

In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.

Authors:

• Amdouni Hamida
• Gammoudi Mohamed Mohsen

Keywords:

• Association rules
• Rule-based classification
• Classification quality
• Validation.

References:

[1] Amdouni H. and Gammoudi M. M. “CondClose: A new algorithm of
association rules extraction”. IJCSI (International Journal of Computer
Science Issues), Volume 8, Issue 4, July (2011).
[2] Bastide Y., Pasquier N., Taouil R., Lakhal L. and Stumme G. “Mining
minimal non-redundant association rules using frequent closed
itemsets”. Proceedings of the Intl. Conference DOOD’2000, LNCS,
Springer-verlag, p. 972-986 (2000).
[3] Ben yahia S., Latiri C., Mineau G.W. and Jaoua A. “Découverte des
règles associatives non redondantes – application aux corpus textuels”.
In M.S. Hacid, Y. Kodrattof and D. Boulanger, editors EGC, volume 17
of Revue des Sciences Technologies de l’Information – série RIA ECA,
pages 131-144. Hermes Sciences Publications (2003).
[4] Ben Yahia S. and Mephu Nguifo E. “Visualisation des règles
associations : vers une approche méta-cognitive”, dans Actes
conférences INFORSID, Hammamet, pp 735-750, 30 Mai - 1 Juin
(2006).
[5] Blanchard J. “Un système de visualisation pour l’extraction,
l’évaluation, et l’exploration interactives des règles d’association”.
Thèse de doctorat à l’Ecole Polytechnique de l’Université de Nantes,
soutenue le 24 novembre (2005).
[6] Blanchard J., Guillet F. and Briand H. “Interactive visual exploration of
association rules with rule-focusing methodology”. Knowledge and
Information Systems, vol. 13, num. 1, p. 43-75, Springer (2007).
[7] Bouzouita I., Elloumi S. and Ben Yahia S. “Garc: a new associative
classification approach”. In A. M. Tjoa and J. Trujillo, editors, Proceedings
of 8th International Conference on Data Warehousing and
Knowledge Discovery (DaWaK 2006), Springer-Verlag, 66LNCS 4081,
Krakow, Poland, pages 554-565, 4-8 September (2006).
[8] Bouzouita I. and Elloumi S. “Generic Associative Classification Rules:
A Comparative Study”. International Journal of Advanced Science and
Technology Vol. 33, August (2011).
[9] Cendrowska J. “PRISM: An Algorithm for Inducing Modular Rules.
International Journal of Man-Machine Studies 27(4):349-370 (1987).
[10] Douar B., Latiri C. and Slimani Y. “Approche hybride de classification
supervisée à base de treillis de galois : application à la reconnaissance de
visages”. In Conference Extraction et Gestion des Connaissances, pp.
309-320. RNTI, Sophia-Antipolis (2008).
[11] Fernandes L. A.F. and Garcia A. C. B. “Association Rule Visualization
and Pruning through Response-Style Data Organization and Clustering”.
J. Pavon et al. (Eds.): IBERAMIA 2012, LNAI 7637, pp. 71–80, 2012.
Springer-Verlag Berlin Heidelberg (2012).
[12] Fule P. and Roddick J. F. “Experiences in building a tool for navigating
association rule result sets”. In CRPIT’04: Proceedings of the second
Australasian workshop on information security, data mining, web
intelligence, and software internationalization (J. Hogan, P. Montague,
M. Purvis & C. Steketee, éds.), Australian Computer Society, Inc., p.
103–108 (2004).
[13] Ganter B. and Wille R. “Formal Concept Analysis”. Mathematical
Foundations, Springer, (1999).
[14] Hahsler M. and Chelluboina S. “Visualizing association rules in
hierarchical groups”. In Computing Science and Statistics, Vol. 42, 42nd
Symposium on the Interface: Statistical, Machine Learning, and
Visualization Algorithms (Interface 2011). The Interface Foundation of
North America, June (2011).
[15] Han J., Kamber M. and Pei J. “Data Mining: Concepts and Techniques,
3rd edition”, Morgan Kaufmann (2011).
[16] http://www.cs.waikato.ac.nz/ml/Weka
[17] http://www.ics.uci.edu/~mlearn/MLRepository.html
[18] Lenca P., Meyer P. and Vaillant B. “Evaluation et analyse multicritère
des mesures de qualité des règles d’associations”. National Journal of
Information Technologies (RNTI), France, pp.219-246 (2004).
[19] Liquiere M. et Mephu Nguifo E. “Legal: learning with galois lattice”. In
Proceeding of the 5ieme Journée sur l’Apprentissage (JFA’90), Lannion,
FRANCE, Avril (1990).
[20] Liu B., Hsu W. and Ma Y. “ Integrating classification and association
rule mining”. InKDD'98 (1998).
[21] Louizi Mehdi and Gammoudi Mohamed Mohsen, “Method for
Classification of Images in the Medical Field: The Nose
Case”, International Journal of Information and Electronics Engineering,
Vol. 2, No. 5, September 2012.
[22] Loan T. T. N., Bay V., Tzung-Pei H. and Hoang Chi T. “CAR-Miner:
An efficient algorithm for mining class-association rules”. Expert Syst.
Appl. 40(6): 2305-2311 (2013).
[23] Ma Y., Liu B. et Wong C. K. “Web for data mining: organizing and
interpreting the discovered rules using the web”. SIGKDD Explorations
2, no. 1, p. 16–23 (2000).
[24] Maddouri M. “Towards a machine learning approach based on
incremental concept formation”. Intelligent Data Analysis, 8(3): 267-280
(2004).
[25] Maddouri M. and Gammoudi J. “On Semantic Properties of
Interestingness Measures for Extracting Rules from Data”. B.
Beliczynski et al. (Eds.): ICANNGA 2007, Part I, LNCS 4431, pp. 148–
158, Springer-Verlag Berlin Heidelberg (2007).
[26] Meddouri N. and Maddouri M. “Boosting Formal Concepts to Discover
Classification Rules”. In Proceeding of the 22rd International
Conference on Industrial, Engineering & Other Applications of Applied
Intelligent Systems (IEA-AIE'09), Tainan, TAIWAN (2009).
[27] Njiwoua P. and MephuNguifo E. “Améliorer l’apprentissage à partir
d’instances grâce à l’induction de concepts”. Revue d’intelligence
artificielle, 13(2): 413-440 (1999).
[28] Olson D. L. and Delen D. “Advanced Data Mining Techniques”.
Springer, 1st edition, page 138, ISBN 3-540-76916-1, (2008).
[29] Oosthuizen G. D. “The use of a lattice in knowledge processing”. PhD
thesis, Glasgow, Scotland, UK (1988).
[30] Pasquier N. “Data mining : algorithmes d'extraction et de réduction des
règles d'association dans les bases de données”. Thèse de doctorat,
Université de Clermont-Ferrand II, (2000).
[31] Quinlan J.R. “C4.5: Programs for Machine Learning”. Morgan Kaufman
Publishers (1993).
[32] Sahami M. “Learning classification rules using lattices (extended
abstract)”. In Proceedings of the 8th European Conference on machine
learning (ECML’95), Heraclion, Crete, GREECE (1995).
[33] Vaillant B., Menou S., Moga S., Lenca P. and Lallich S. “Qualité des
règles d’association : étude de données d’entreprise”. In Atelier Data
mining dans la banque, l’assurance et la finance (associé à la conférence
Extraction et Gestion des Connaissances (2007)), pp.45—54, Namur,
Belgique (2007).
[34] Wang J. and G. Karypis. “HARMONY: Efficiently mining the best rules
for classification”. In SIAM'05 (2005).
[35] Zaki M. and Phoophakdee B. “MIRAGE: A framework for mining,
exploring and visualizing minimal association rules”. Technical report,
July 2003, Rensselaer Polytechnic Institute, Computer Sciences
Department, USA (2003).