Optimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining | ||
| Journal of AI and Data Mining | ||
| دوره 8، شماره 4، بهمن 2020، صفحه 491-514 اصل مقاله (2.89 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2020.8928.2029 | ||
| نویسندگان | ||
| Z. Anari1؛ A. Hatamlou* 2؛ B. Anari3؛ M. Masdari4 | ||
| 1Department of Computer Engineering and Information Technology, Payame Noor University (PNU), P. OBox,19395-4697 Tehran, Iran | ||
| 2Department of Computer Engineering, Khoy Branch, Islamic Azad University, Khoy, Iran. | ||
| 3Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran. | ||
| 4Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran | ||
| چکیده | ||
| The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since the membership functions of each web page are different from those of other web pages, so automatic finding the number and position of TMF is significant. In this paper, a different reinforcement-based optimization approach called LA-OMF was proposed to find both the number and positions of TMFs for fuzzy association rules. In the proposed algorithm, the centers and spreads of TMFs were considered as parameters of the search space, and a new representation using learning automata (LA) was proposed to optimize these parameters. The performance of the proposed approach was evaluated and the results were compared with the results of other algorithms on a real dataset. Experiments on datasets with different sizes confirmed that the proposed LA-OMF improved the efficiency of mining fuzzy association rules by extracting optimized membership functions. | ||
| کلیدواژهها | ||
| web usage mining؛ learning automata؛ fuzzy set؛ membership function؛ fuzzy association rule | ||
| مراجع | ||
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[1] Etzioni, O. (1996). The world wide web: Quagmire or gold mine? Communications of the ACM, vol. 39, no. 11. [2] Cooley, R., Mobasher, B., and Srivastava, J. (1997). Web Mining: Information and Pattern Discovery on the World Wide Web. In: ictai, pp. 558-567. [3] Kosala, R., & Blockeel, H. (2000). Web mining research: A survey. ACM Sigkdd Explorations Newsletter, vol. 2, pp. 1-15. [4] Mobasher, B., Dai, H., Luo, T., Sun, Y., & Zhu, J. (2000). Integrating web usage and content mining for more effective personalization. In: International Conference on Electronic Commerce and Web Technologies: Springer, pp. 165-176. [5] Cho, Y. H., Kim, J. K., and Kim, S.H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications, vol. 23, pp. 329-342. [6] Eirinaki, M., and Vazirgiannis, M. (2003). Web mining for web personalization. ACM Transactions on Internet Technology (TOIT), vol. 3, pp. 1-27. [7] Pei, J., Han, J., Mortazavi-Asl, B., & Zhu, H. (2000). Mining access patterns efficiently from web logs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining: Springer, pp. 396-407. [8] Castellano, G., Fanelli, A., and Torsello, M. (2007). LODAP: a log data preprocessor for mining web browsing patterns. In: Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases: Citeseer, pp. 12-17. [9] Sisodia, D. S., Khandal, V., & Singhal, R. (2018). Fast prediction of web user browsing behaviours using most interesting patterns. Journal of Information Science, vol. 44, pp. 74-90. [10] Malarvizhi, S., & Sathiyabhama, B. (2016). Frequent pagesets from web log by enhanced weighted association rule mining. Cluster Computing, vol. 19, pp. 269-277. [11] Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pp. 207-216. [12] Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A.I. (1996). Fast discovery of association rules. Advances in knowledge discovery and data mining, vol. 12, pp. 307-328. [13] Zadeh, L. A. (1965). Fuzzy sets. Information and control, vol. 8, pp. 338-353. [14] Lopez, F. J., Blanco, A., Garcia, F., & Marin, A. (2007). Extracting biological knowledge by fuzzy association rule mining. In: 2007 IEEE International Fuzzy Systems Conference: IEEE, pp. 1-6. [15] Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. In: Proceedings of the institution of electrical engineers: IET, pp. 1585-1588. [16] Tajbakhsh, A., Rahmati, M., & Mirzaei, A. (2009). Intrusion detection using fuzzy association rules. Applied Soft Computing, vol. 9, pp. 462-469. [17] Wang, M., Su, X., Liu, F., & Cai, R. (2012). A cancer classification method based on association rules. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery: IEEE, pp. 1094-1098. [18] Watanabe, T., & Fujioka, R. (2012). Fuzzy association rules mining algorithm based on equivalence redundancy of items. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC): IEEE, pp. 1960-1965. [19] Weber, R. (1992). A class of methods for automatic knowledge acquisition. In: Proc. Of the 2nd International Conference on Fuzzy Logic and Neural Networks, 1992. [20] Kudłacik, P., Porwik, P., & Wesołowski, T. (2016). Fuzzy approach for intrusion detection based on user’s commands. Soft Computing, vol. 20, pp. 2705-2719. [21] Wu, R., Tang, W., & Zhao, R. (2005). Web mining of preferred traversal patterns in fuzzy environments. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing: Springer, pp. 456-465. [22] Lin, C. W., & Hong, T. P. (2013). A survey of fuzzy web mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 3, pp. 190-199. [23] Ansari, Z. A., & Syed, A. S. (2016). Discovery of web usage patterns using fuzzy mountain clustering. International Journal of Business Intelligence and Data Mining, vol. 11, pp. 1-18. [24] Ansari, Z. A., Sattar, S. A., & Babu, A. V. (2017). A fuzzy neural network based framework to discover user access patterns from web log data. Advances in Data Analysis and Classification, vol. 11, pp. 519-546. [25] Hong, T.-P., Huang, C.-M., & Horng, S.-J. (2008). Linguistic object-oriented web-usage mining. International journal of approximate reasoning, vol. 48, pp. 47-61. [26] Hong, T.-P., Chiang, M.-J., & Wang, S.-L. (2002). Mining weighted browsing patterns with linguistic minimum supports. In: IEEE International Conference on Systems, Man and Cybernetics: IEEE, vol. 4, pp. 5-pp. IEEE. [27] Hong, T.-P., Chiang, M.-J., & Wang, S.-L. (2008). Mining fuzzy weighted browsing patterns from time duration and with linguistic thresholds. [28] Wang, S.-L., Lo, W.-S., & Hong, T.-P. (2005) Discovery of fuzzy multiple-level Web browsing patterns. In: Classification and Clustering for Knowledge Discovery: Springer, pp. 251-266. [29] Wu, R. (2010). Mining generalized fuzzy association rules from Web logs. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery: IEEE, pp. 2474-2477. [30] Narendra, K. S., & Thathachar, M. A. (2012). Learning automata: an introduction. Courier Corporation. [31] Thathachar, M. A., & Sastry, P. S. (2011). Networks of learning automata: Techniques for online stochastic optimization. Springer Science & Business Media. [32] Thathachar, M. A., & Sastry, P. S. (2002). Varieties of learning automata: an overview. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 32, pp. 711-722. [33] Hong, T.-P., Chen, C.-H., Wu, Y.-L., & Lee, Y.-C. (2006). A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Computing, vol. 10, pp. 1091-1101. [34] Chen, C.-H., Tseng, V. S., &Hong, T.-P. (2008) Cluster-based evaluation in fuzzy-genetic data mining. IEEE transactions on fuzzy systems, vol. 16, pp. 249-262. [35] Alcalá-Fdez, J., Alcalá, R., Gacto, M. J., & Herrera, F. (2009). Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets and Systems, vol. 160, pp. 905-921. [36] Chen, C.-H., Li, Y., Hong, T.-P., Li, Y.-K., & Lu, E.H.-C. (2015). A GA-based approach for mining membership functions and concept-drift patterns. In: 2015 IEEE Congress on Evolutionary Computation (CEC): IEEE, pp. 2961-2965. [37] Chen, C.-H., Hong, T.-P., Lee, Y.-C., & Tseng, V.S. (2015). Finding active membership functions for genetic-fuzzy data mining. International Journal of Information Technology & Decision Making, vol. 295, pp. 358-378. [39] Hong, T.-P., Tung, Y.-F., Wang, S.-L., Wu, M.-T., and Wu, Y.-L. (2009). An ACS-based framework for fuzzy data mining. Expert Systems with Applications, vol. 36, pp. 11844-11852. [40] Wu, M.-T., Hong, T.-P., & Lee, C.-N. (2012). A continuous ant colony system framework for fuzzy data mining. Soft Computing, vol. 16, pp. 2071-2082. [41] Ting, C.-K., Liaw, R.-T., Wang, T.-C., & Hong, T.-P. (2018). Mining fuzzy association rules using a memetic algorithm based on structure representation. Memetic Computing, vol. 10, pp. 15-28. [42] Ting, C.-K., Wang, T.-C., Liaw, R.-T., & Hong, T.-P. (2017). Genetic algorithm with a structure-based representation for genetic-fuzzy data mining. Soft Computing, vol. 21, pp. 2871-2882. [43] Rudziński, F. (2016). A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Applied Soft Computing, vol. 38, pp. 118-133. [44] Antonelli, M., Ducange, P., & Marcelloni, F. (2014). A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Information Sciences, vol. 283, pp. 36-54. [45] Minaei-Bidgoli, B., Barmaki, R., & Nasiri, M. (2013). Mining numerical association rules via multi-objective genetic algorithms. Information Sciences, vol. 233, pp. 15-24. [46] Song, A., Song, J., Ding, X., Xu, G., & Chen, J. (2017). Utilizing bat algorithm to optimize membership functions for fuzzy association rules mining. In: International Conference on Database and Expert Systems Applications: Springer, pp. 496-504. [47] Chamazi, M.A., & Motameni, H. (2019) Finding suitable membership functions for fuzzy temporal mining problems using fuzzy temporal bees method. Soft Computing, vol. 23, pp. 3501-3518. [48] Alikhademi, F., & Zainudin, S. (2014). Generating of derivative membership functions for fuzzy association rule mining by Particle Swarm Optimization. In: 2014 International Conference on Computational Science and Technology (ICCST): IEEE, pp. 1-6. [49] Hong, T.-P., Lee, Y.-C., & Wu, M.-T. (2014). An effective parallel approach for genetic-fuzzy data mining. Expert Systems with Applications, vol. 41, pp. 655-662. [50] Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In: Acm sigmod record: ACM, pp. 207-216. [51] TSetlin, M., & TSetlin, M. (1973). Automaton theory and modeling of biological systems. [52] Lakshmivarahan, S. (2012). Learning Algorithms Theory and Applications: Theory and Applications. Springer Science & Business Media. [53] Meybodi, M., & Lakshmivarahan, S. (1984). On a class of learning algorithms which have a symmetric behavior under success and failure. Lecture Notes in Statistics, Berlin: SpringerVerlag, pp. 145-155. [54] Meybodi, M.R., & Beigy, H. (2002). New learning automata based algorithms for adaptation of backpropagation algorithm parameters. International Journal of Neural Systems, vol. 12, pp. 45-67. [55] Ghavipour, M., & Meybodi, M.R. (2018). A streaming sampling algorithm for social activity networks using fixed structure learning automata. Applied Intelligence, vol. 48, pp. 1054-1081. [56] Narendra, K.S., & Thathachar, M.A. (1980). On the behavior of a learning automaton in a changing environment with application to telephone traffic routing. IEEE Transactions on Systems, Man, and Cybernetics, vol. 10, pp. 262-269. [57] Anari, B., Torkestani, J. A., & Rahmani, A. M. (2017). Automatic data clustering using continuous action-set learning automata and its application in segmentation of images. Applied Soft Computing, vol. 51, pp. 253-265. [58] Ghavipour, M., & Meybodi, M.R. (2016). An adaptive fuzzy recommender system based on learning automata. Electronic Commerce Research and Applications, vol. 20, pp. 105-115. [59] Kumar, N., Lee, J.-H., & Rodrigues, J. J. (2014). Intelligent mobile video surveillance system as a Bayesian coalition game in vehicular sensor networks: Learning automata approach. IEEE Transactions on Intelligent Transportation Systems, vol. 16, pp. 1148-1161. [60] Helmzadeh, A., & Kouhsari, S. M. (2016). Calibration of erroneous branch parameters utilising learning automata theory. IET Generation, Transmission & Distribution, vol. 10, pp. 3142-3151. [61]Torkestani, J.A. (2012). An adaptive learning automata-based ranking function discovery algorithm. Journal of intelligent information systems, vol. 39, pp. 441-459. [62] Morshedlou, H., & Meybodi, M. R. (2014). Decreasing impact of sla violations: a proactive resource allocation approachfor cloud computing environments. IEEE Transactions on Cloud Computing, vol. 2, pp. 156-167. [63] Rezvanian, A., & Meybodi, M.R. (2010). Tracking extrema in dynamic environments using a learning automata-based immune algorithm. In: Grid and Distributed Computing, Control and Automation: Springer, pp. 216-225. [64] Anari, B., Akbari Torkestani, J., & Rahmani, A.M. (2018). A learning automata‐based clustering algorithm using ant swarm intelligence. Expert systems, vol. 35, no. 6, e12310. [65] Hong, T.-P., Chen, C.-H., Lee, Y.-C., & Wu, Y.-L. (2008). Genetic-fuzzy data mining with divide-and-conquer strategy. IEEE Transactions on Evolutionary Computation, vol. 12, pp. 252-265. [66] Tao, Y.-H., Hong, T.-P., Lin, W.-Y., &Chiu, W.-Y. (2009). A practical extension of web usage mining with intentional browsing data toward usage. Expert Systems with Applications, vol. 36, pp. 3937-3945. [67] http://www.cs.depaul.edu. [68] Nosratian, F., Nematzadeh, H., & Motameni, H. (2019). A Technique for improving Web mining using enhanced genetic algorithm. Journal of AI and Data Mining, vol. 7, no. 4, pp. 597-606. [69] Azimi Kashani, A., Ghanbari, M., & Rahmani, A. M. (2020). Improving performance of opportunistic routing protocol using fuzzy logic for vehicular ad-hoc networks in highways. Journal of AI and Data Mining, vol. 8 , no. 2, pp. 213-226. [70] Roohollahi, S., Khatibi Bardsiri, A., & Keynia, F. (2020). Using an evaluator fixed structure learning automata in sampling of social networks. Journal of AI and Data Mining, vol. 8, no. 1, pp. 127-148. [71] Vaghei, Y., & Farshidianfar, A. (2016). Trajectory tracking of under-actuated nonlinear dynamic robots: adaptive fuzzy hierarchical terminal sliding-mode control. Journal of AI and Data Mining, vol. 4, no. 1, pp. 93-102. [72] Hatamlou, A. R., & Deljavan, M. (2019). Forecasting gold price using data mining techniques by considering new factors. Journal of AI and Data Mining, vol. 7, no. 3, pp. 411-420. | ||
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