Towards an enhanced user’s preferences integration into ranking process using dominance approach
Tóm tắt
User preference is very important in orienting data miner, and this is the reason why these user preferences are integrated in the mining process, where they are coupled with Association Rules Mining “ARM” Algorithms to select only Association Rules “ARs” that satisfy the user’s wishes and expectations. Within this framework, several approaches were proposed to overcome some problems which persist with the traditional ARM algorithms mainly dimensionality phenomenon engendered by thresholding and the subjective choice of measures. “MDP
$$_{\mathrm {REF}}$$
Algorithm” is one of these approaches; it prunes, filters to select the relevant ARs, while ”Rank-Sort-MDP
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” sorts, ranks, and stores ARs to complete the MDP
$$_{\mathrm {REF}}$$
algorithm mining operation. Experiment result on real database showed the advantages of MDP
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algorithm and Rank-Sort-MDP
$$_{\mathrm {REF}}$$
algorithm over the other algorithms.
Tài liệu tham khảo
Ait-Mlouk, A., Gharnati, F., Agouti, T.: Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach. Vietnam J. Comput. Sci. 3(4), 235–245 (2016). doi:10.1007/s40595-016-0070-4
Arvanitis, A., Koutrika, G.: PrefDB: supporting preferences as first-class citizens in relational databases. IEEE Trans. Knowl. Data Eng. 26(6), 1430–1446 (2014). doi:10.1109/TKDE.2013.28
Asha, P., Srinivasan, S.: Analysing the associations between infected genes using data mining techniques. Int. J. Data Mining Bioinf. 15(3), 250–271 (2016). doi:10.1504/IJDMB.2016.0770
Bouker, S., Saidi, R., Ben Yahia, S., Mephu Nguifo, E.: Mining undominated association rules through interestingness measures. Int J Artif. Intell. Tools. 23(4), 1460011 (2014). doi:10.1142/S0218213014600112
Branke, J., Corrente, S., Greco, S., Słowiński, R., Zielniewicz, P.: Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. Eur. J. Oper. Res. 250(3), 884–901 (2016). doi:10.1016/j.ejor.2015.10.027
Branke, J.: MCDA and multiobjective evolutionary algorithms. Multiple Criteria Decision Analysis, pp. 977–1008 (2016). doi:10.1007/978-1-4939-3094-4_23
De Amo, S., Saliou Diallo, M., Talibouya Diop, C., Giacometti, A., Li, D., Soulet, A.: Contextual preference mining for user profile construction. Inf. Syst. 49, 182–199 (2015). doi:10.1016/j.is.2014.11.009
Gheorghiu, R., Labrinidis, A., Chrysanthis, P.: Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization. Proceedings of the Second International Workshop on Databases and the Web - ExploreDB’15, pp. 6–8 (2015). doi:10.1145/2795218.2795223
Gupta, G.: Introduction to data mining with case studies. PHI Learning Pvt, Ltd (2014)
Jiang, B., Pei, J., L, X., Cheung, D., Han, J.: Mining preferences from superior and inferior examples. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 390–398 (2008)
Kongchai, P., Kerdprasop, N., Kerdprasop, K.: Dissimilar Rule Mining and Ranking Technique for Associative Classification. Proceedings of the International MultiConference of Engineers and Computer Scientists 2013, IMECS 2013. 1 (2013)
Mallik, S., Mukhopadhyay, A., Maulik, U.: RANWAR: Rank-based weighted association rule mining from gene expression and methylation data. IEEE Trans. NanoBiosci. 14(1), 59–66 (2015)
Mehrotra, A., Hendley, R., Musolesi, M.: PrefMiner. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing-UbiComp ’16, pp. 1223–1234 (2016). doi:10.1145/2971648.2971747
Miao, X., Gao, Y., Chen, G., Cui, H., Guo, C., Pan, W.: Si2p: a restaurant recommendation system using preference queries over incomplete information. Proc. VLDB Endow. 9(13), 1509–1512 (2016). doi:10.14778/3007263.3007296
Mouhir, M., Gadi, T., Balouki, Y., El Far, M.: A new way to select the valuable association rules. 2015 7th International Conference on Knowledge and Smart Technology (KST), pp. 81–86 (2015). doi:10.1109/KST.2015.7051464
Najeeb, M. M., El Sheikh, A., Nababteh, M.: A new rule ranking model for associative classification using a hybrid artificial intelligence technique. In: Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on IEEE, pp. 231–235 (2011)
Rolfsnes, T., Moonen, L., Di Alesio, S., Behjati, R., Binkley, D.: Improving change recommendation using aggregated association rules. Proceedings of the 13th International Workshop on Mining Software Repositories—MSR ’16, pp. 73–84 (2016). doi:10.1145/2901739.2901756
Shmueli, G., Peter Bruce, C., Nitin, Patel R.: Data mining for business analytics: concepts, techniques, and applications with XLMiner. Wiley, Hoboken (2016)
Soulet, A., Raïssi, C., Plantevit, M., Cremilleux, B.: Mining Dominant Patterns in the Sky. 2011 IEEE 11th International Conference on Data Mining, pp. 655–664 (2011). doi:10.1109/ICDM.2011.100
Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B., Lepailleur, A.: Mining (Soft-) skypatterns using constraint programming. Advances in Knowledge Discovery and Management, pp. 105–136 (2015). doi:10.1007/978-3-319-23751-0_6
Yang, G., Mabu, S. M., Shimada, K., Gong, Y., Hirasawa, K.: Ranking association rules for classification based on genetic network programming. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation ACM, pp. 1917–1918 (2009)
Zhang, J., Lin, Y., Lin, M., Liu, J.: An effective collaborative filtering algorithm based on user preference clustering. Appl. Intell. 45(2), 230–240 (2016). doi:10.1007/s10489-015-0756-9
Zhang, J., Jiang, X., Ku, W.S., Qin, X.: Efficient parallel skyline evaluation using mapreduce. IEEE Trans. Parallel Distrib. Syst. 27(7), 1996–2009 (2016)
Zhu, H., Chen, E., Xiong, H., Yu, K., Cao, H., Tian, J.: Mining mobile user preferences for personalized context-aware recommendation. ACM Trans. Intell. Syst. Technol. 5(4), 1–27 (2014). doi:10.1145/253251