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Selecting and Filtering Association Rules within a Semantic Technique


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DOI: https://doi.org/10.15866/irecos.v11i6.9556

Abstract


To tackle the problems of thresholding, redundancy, and overlapping from which the Association Rules (AR) suffer, this paper suggests an approach, namely SEMMDPREF, to discover interesting rules by pruning and filtering them. Our approach introduces the notions of dominance between a rule and that of user preference, in addition to the methods and techniques based on semantic significance to efficiently exploit the Ontology Domain (OD). This three-angled approach aims to select the semantically significant, most dominant and preferential rules. Concerning algorithm evaluation, we use a genuine database. The results issued from our experiments show that the OD consideration allows our approach to: Detect and discard rules which are already known and which may be meaningless too. Be able to decrease considerably the size of AR by keeping the ones satisfying the criteria.
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Keywords


Ontology; Association Rules Mining; Semantic Analysis; SEMMDPREF Algorithm; MDPREF Algorithm

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References


M. Kamber, J. Han, Data Mining: Concept and Techniques (The Morgan Kaufmann Series in Data Management Systems, 2006).

Mouhir, M., Balouki, Y., Gadi, T. El Far, M. A new way to select the valuable association rules, Proceedings of the Knowledge and Smart Technology (KST), 2015 7th International Conference on, 2015 7th KST IEEE, (Page: 81-86 Year Of Publication : 2015).
http://dx.doi.org/10.1109/kst.2015.7051464

S. Bouker, R. Saidi, S. B. Yahia, E. M. Nguifo. Mining Undominated Association Rules Through Interestingness Measures, (2014) International Journal on Artificial Intelligence Tools, 23 (4), pp.1-22.
http://dx.doi.org/10.1142/s0218213014600112

C. Marinica, F. Guillet, Knowledge-based interactive postmining of association rules using ontologies, (2010) IEEE Transaction On Knowledge and Data Engineering, 22(6) , pp.784-797.
http://dx.doi.org/10.1109/tkde.2010.29

Chen, P., Verma, R. M., Meininger, J. C., Chan, W. Semantic Analysis of Association Rules, Proceeding of Twenty-First International Florida Artificial Intelligence Research Society Conference- FLAIRS'21(Page : 270-27 Year of Publication : 2008).

P. Ristoski, H. Paulheim. Semantic Web in data mining and knowledge discovery: A comprehensive survey, (2016) Web Semantics: Science, Services and Agents on the World Wide Web, 36, pp.1-22.
http://dx.doi.org/10.1016/j.websem.2016.01.001

S. Bouker, R. Saidi, S. B. Yahia, E. M. Nguifo. Towards a semantic and statistical selection of association rules, (2013) the Computing Research Repository (CoRR).

Diana B. P. (2015), MOGACAR: A Method for Filtering Interesting Classification. Proceeding of 11th International Conference, MLDM 2015 ( Page : 172–183 Year of Publication : 2015).

S. De Amo, M. S. Diallo, C. T. Diop, A. Giacometti, D. Li, A. Soulet, Contextual preference mining for user profile construction, (2015) Journal Information System, 49, pp. 182-199.
http://dx.doi.org/10.1016/j.is.2014.11.009

Chen, X., Zhou, X., Scher, R., Geller, J. Using an interest Ontology for Improved Support in Rule Mining, Proceedings of the 5th International Conference of Data Warehousing and Knowledge Discovery (Page : 320-329 Year of Publication : 2003).
http://dx.doi.org/10.1007/978-3-540-45228-7_32

Hou, X., Gu, J., Shen, X., Yan, W. Application of data mining in fault diagnosis based on ontology, Proceedings of the Third IEEE International Conference on Information Technology and Applications ( Page: 260-263 Year of Publication: 2005).
http://dx.doi.org/10.1109/icita.2005.70

P. Asha, T. Jebarajan, V. Cyriac Thomas. Efficient mining of Frequent Itemsets and Association Rules, (2014) Australian Journal of Basic and Applied Sciences, 8 (7) pp. 510-517.

Talib, A., Alomary, F., Towards a Comprehensive Ontology Based-Investigation for Digital Forensics Cybercrime, (2015) International Journal on Communications Antenna and Propagation (IRECAP), 5 (5), pp. 263-268.
http://dx.doi.org/10.15866/irecap.v5i5.6112

Khaled, R., Tayeb, L., Okba, K., Servigne, S., Geospatial Web Services Semantic Discovery Approach Using Metadata and Multi-Agents System, (2014) International Journal on Information Technology (IREIT), 2 (4), pp. 124-130.

Benslimane, S., Mimoun, M., Bouchiha, D., Benslimane, D., OntoWer: an Ontology Based Web Application Reverse-Engineering Approach, (2014) International Journal on Information Technology (IREIT), 2 (5), pp. 151-157.

Djaghloul, Y., Boufaida, Z., Toward Peer to Peer Platform Integration based on OWL Ontology and Roaming Service, (2014) International Journal on Information Technology (IREIT), 2 (6), pp. 195-206.

M. Rani, R. Nayak, O. P. Vyas, An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage, (2015) Knowledge-Based Systems, 90, pp. 33-48.
http://dx.doi.org/10.1016/j.knosys.2015.10.002

D. Sadoun, Des spécifications en langage naturel aux spécifications formelles via une ontologie comme model pivot. PhD thesis, University of Paris XI, Paris, French, 2014.

A.Maedche, S. Staab, Ontology Learning for the Semantic Web, (2001) IEEE Intelligent System, 16 (2), pp.72-79.
http://dx.doi.org/10.1109/5254.920602

Mouhir, M., Dahbi, A., Balouki, Y., Gadi, T. SEMMDPREF : Algorithm to filter and sort rules using a semantically based ontology technique, Proceedings of the 7th International Conference on Management of computational and collective intElligence in Digital EcoSystems - ACM-MEDES’15 (Page : 29-34 Year of Publication: 2015).
http://dx.doi.org/10.1145/2857218.2857223


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