[1] Etzioni, O., The world {wide web: Quagmire or gold mine? Communications of the ACM, 39 (11): 65 {68. 1996, November.
[2] Cooley, R., B. Mobasher, and J. Srivastava. Web Mining: Information and Pattern Discovery on the World Wide Web. in ictai. 1997.
[3] Kosala, R. and H. Blockeel, Web mining research: A survey. ACM Sigkdd Explorations Newsletter, 2000. 2(1): p. 1-15.
[4] Kalaignanam, K., T. Kushwaha, and K. Rajavi, How does web personalization create value for online retailers? Lower cash flow volatility or enhanced cash flows. Journal of Retailing, 2018. 94(3): p. 265-279.
[5] Wagh, R. and J. Patil, Enhanced web personalization for improved browsing experience. Advances in Computational Sciences and Technology, 2017. 10(6): p. 1953-1968.
[6] Desai, D., An empirical study of website personalization effect on users intention to revisit E-commerce website through cognitive and hedonic experience, in Data Management, Analytics and Innovation. 2019, Springer. p. 3-19.
[7] Sharma, S. and V. Rana, Web Search Personalization Using Semantic Similarity Measure, in Proceedings of ICRIC 2019. 2020, Springer. p. 273-288.
[8] Kaur, J. and J.S. Bal. Machine Learning Approach to Recommender System for Web Mining. in International Conference on Intelligent Data Communication Technologies and Internet of Things. 2019. Springer.
[9] Moreno, M.N., et al., Web mining based framework for solving usual problems in recommender systems. A case study for movies׳ recommendation. Neurocomputing, 2016. 176: p. 72-80.
[10] Manikandan, R. and V. Saravanan, A novel approach on Particle Agent Swarm Optimization (PASO) in semantic mining for web page recommender system of multimedia data: a health care perspective. Multimedia Tools and Applications, 2020. 79(5): p. 3807-3829.
[11] Anandhi, D. and M.I. Ahmed, Prediction of user’s type and navigation pattern using clustering and classification algorithms. Cluster Computing, 2019. 22(5): p. 10481-10490.
[12] El Aissaoui, O., et al., A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Education and Information Technologies, 2019. 24(3): p. 1943-1959.
[13] Van, T., A. Yoshitaka, and B. Le, Mining web access patterns with super-pattern constraint. Applied Intelligence, 2018. 48(11): p. 3902-3914.
[14] Xiong, L., et al., Access patterns mining from massive spatio-temporal data in a smart city. Cluster Computing, 2019. 22(3): p. 6031-6041.
[15] Liu, Z., et al., Patterns and sequences: Interactive exploration of clickstreams to understand common visitor paths. IEEE Transactions on Visualization and Computer Graphics, 2016. 23(1): p. 321-330.
[16] Sethi, S. and A. Dixit, A novel page ranking mechanism based on user browsing patterns, in Software Engineering. 2019, Springer. p. 37-49.
[17] Vojíř, S., et al., EasyMiner. eu: Web framework for interpretable machine learning based on rules and frequent itemsets. Knowledge-Based Systems, 2018. 150: p. 111-115.
[18] Rekik, R., et al., Assessing web sites quality: A systematic literature review by text and association rules mining. International Journal of Information Management, 2018. 38(1): p. 201-216.
[19] Kumar, M. and M. Meenu, A survey on pattern discovery of web usage mining. International Journal of Advance Research, Ideas and Innovations in Technology, 2017. 3(1): p. 379-385.
[20] Sisodia, D.S., V. Khandal, and R. Singhal, Fast prediction of web user browsing behaviours using most interesting patterns. Journal of Information Science, 2018. 44(1): p. 74-90.
[21] Malarvizhi, S. and B. Sathiyabhama, Frequent pagesets from web log by enhanced weighted association rule mining. Cluster Computing, 2016. 19(1): p. 269-277.
[22] Zadeh, L.A., Fuzzy sets. Information and control, 1965. 8(3): p. 338-353.
[23] Lopez, F.J., et al. Extracting biological knowledge by fuzzy association rule mining. in 2007 IEEE International Fuzzy Systems Conference. 2007. IEEE.
[24] Mamdani, E.H. Application of fuzzy algorithms for control of simple dynamic plant. in Proceedings of the institution of electrical engineers. 1974. IET.
[25] Tajbakhsh, A., M. Rahmati, and A. Mirzaei, Intrusion detection using fuzzy association rules. Applied Soft Computing, 2009. 9(2): p. 462-469.
[26] Wang, M., et al. A cancer classification method based on association rules. in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery. 2012. IEEE.
[27] Watanabe, T. and R. Fujioka. Fuzzy association rules mining algorithm based on equivalence redundancy of items. in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2012. IEEE.
[28] Weber, R. A class of methods for automatic knowledge acquisition. in Proc. Of the 2nd International Conference on Fuzzy Logic and Neural Networks, 1992. 1992.
[29] Kudłacik, P., P. Porwik, and T. Wesołowski, Fuzzy approach for intrusion detection based on user’s commands. Soft Computing, 2016. 20(7): p. 2705-2719.
[30] Nasseri, S.H., A. Ebrahimnejad, and O. Gholami, Fuzzy stochastic data envelopment analysis with undesirable outputs and its application to banking industry. International Journal of Fuzzy Systems, 2018. 20(2): p. 534-548.
[31] Molina, C., M.D. Ruiz, and J.M. Serrano, Representation by levels: An alternative to fuzzy sets for fuzzy data mining. Fuzzy Sets and Systems, 2019.
[32] Sumathi, G. and J. Akilandeswari, Improved fuzzy weighted‐iterative association rule based ontology postprocessing in data mining for query recommendation applications. Computational Intelligence, 2020.
[33] Wu, R., W. Tang, and R. Zhao. Web mining of preferred traversal patterns in fuzzy environments. in International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. 2005. Springer.
[34] Lin, C.W. and T.P. Hong, A survey of fuzzy web mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2013. 3(3): p. 190-199.
[35] Ansari, Z.A. and A.S. Syed, Discovery of web usage patterns using fuzzy mountain clustering. International Journal of Business Intelligence and Data Mining, 2016. 11(1): p. 1-18.
[36] Ansari, Z.A., S.A. Sattar, and A.V. Babu, A fuzzy neural network based framework to discover user access patterns from web log data. Advances in Data Analysis and Classification, 2017. 11(3): p. 519-546.
[37] Hamidzadeh, J., M. Zabihimayvan, and R. Sadeghi, Detection of Web site visitors based on fuzzy rough sets. Soft Computing, 2018. 22(7): p. 2175-2188.
[38] Hong, T.-P., C.-M. Huang, and S.-J. Horng, Linguistic object-oriented web-usage mining. International Journal of Approximate Reasoning, 2008. 48(1): p. 47-61.
[39] Hong, T.-P., M.-J. Chiang, and S.-L. Wang, Mining fuzzy weighted browsing patterns from time duration and with linguistic thresholds. 2008.
[40] Matthews, S.G., et al., Web usage mining with evolutionary extraction of temporal fuzzy association rules. Knowledge-Based Systems, 2013. 54: p. 66-72.
[41] Wang, S.-L., W.-S. Lo, and T.-P. Hong, Discovery of fuzzy multiple-level Web browsing patterns, in Classification and Clustering for Knowledge Discovery. 2005, Springer. p. 251-266.
[42] Narendra, K.S. and M.A. Thathachar, On the behavior of a learning automaton in a changing environment with application to telephone traffic routing. IEEE Transactions on Systems, Man, and Cybernetics, 1980. 10(5): p. 262-269.
[43] Anari, B., J.A. Torkestani, and A.M. Rahmani, Automatic data clustering using continuous action-set learning automata and its application in segmentation of images. Applied Soft Computing, 2017. 51: p. 253-265.
[44] Ghavipour, M. and M.R. Meybodi, An adaptive fuzzy recommender system based on learning automata. Electronic Commerce Research and Applications, 2016. 20: p. 105-115.
[45] Kumar, N., J.-H. Lee, and J.J. Rodrigues, Intelligent mobile video surveillance system as a Bayesian coalition game in vehicular sensor networks: Learning automata approach. IEEE Transactions on Intelligent Transportation Systems, 2014. 16(3): p. 1148-1161.
[46] Helmzadeh, A. and S.M. Kouhsari, Calibration of erroneous branch parameters utilising learning automata theory. IET Generation, Transmission & Distribution, 2016. 10(13): p. 3142-3151.
[47] Torkestani, J.A., An adaptive learning automata-based ranking function discovery algorithm. Journal of intelligent information systems, 2012. 39(2): p. 441-459.
[48] Ghasemi, S., et al., A cost-aware mechanism for optimized resource provisioning in cloud computing. Cluster Computing, 2018. 21(2): p. 1381-1394.
[49] Kordestani, J.K., et al., A novel framework for improving multi-population algorithms for dynamic optimization problems: A scheduling approach. Swarm and evolutionary computation, 2019. 44: p. 788-805.
[50] Anari, B., J. Akbari Torkestani, and A.M. Rahmani, A learning automata‐based clustering algorithm using ant swarm intelligence. Expert systems, 2018. 35(6): p. e12310.
[51] Palacios, A.M., et al., Genetic learning of the membership functions for mining fuzzy association rules from low quality data. Information Sciences, 2015. 295: p. 358-378.
[52] Lin, J.C.-W., et al., Efficient mining of multiple fuzzy frequent itemsets. International Journal of Fuzzy Systems, 2017. 19(4): p. 1032-1040.
[53] Chen, C.-H., et al. A GA-based approach for mining membership functions and concept-drift patterns. in 2015 IEEE Congress on Evolutionary Computation (CEC). 2015. IEEE.
[54] Rudziński, F., A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Applied Soft Computing, 2016. 38: p. 118-133.
[55] Chen, C.-H., et al., Finding active membership functions for genetic-fuzzy data mining. International Journal of Information Technology & Decision Making, 2015. 14(06): p. 1215-1242.
[56] Wu, M.-T., T.-P. Hong, and C.-N. Lee, A continuous ant colony system framework for fuzzy data mining. Soft Computing, 2012. 16(12): p. 2071-2082.
[57] Ting, C.-K., et al., Mining fuzzy association rules using a memetic algorithm based on structure representation. Memetic Computing, 2018. 10(1): p. 15-28.
[58] Ting, C.-K., et al., Genetic algorithm with a structure-based representation for genetic-fuzzy data mining. Soft Computing, 2017. 21(11): p. 2871-2882.
[59] Song, A., et al. Utilizing bat algorithm to optimize membership functions for fuzzy association rules mining. in International Conference on Database and Expert Systems Applications. 2017. Springer.
[60] Chamazi, M.A. and H. Motameni, Finding suitable membership functions for fuzzy temporal mining problems using fuzzy temporal bees method. Soft Computing, 2019. 23(10): p. 3501-3518.
[61] Alikhademi, F. and S. Zainudin. Generating of derivative membership functions for fuzzy association rule mining by Particle Swarm Optimization. in 2014 International Conference on Computational Science and Technology (ICCST). 2014. IEEE.
[62] Hong, T.-P., Y.-C. Lee, and M.-T. Wu, An effective parallel approach for genetic-fuzzy data mining. Expert Systems with Applications, 2014. 41(2): p. 655-662.
[63] Patil, U.M. and J. Patil, MINING FUZZY ASSOCIATION RULES FROM WEB USAGE QUANTITATIVE DATA. Computer Science & Information Technology, 2016. 89.
[64] TSetlin, M. and M. TSetlin, Automaton theory and modeling of biological systems. 1973.
[65] Thathachar, M.A. and P.S. Sastry, Networks of learning automata: Techniques for online stochastic optimization. 2011: Springer Science & Business Media.
[66] Narendra, K.S. and M.A. Thathachar, Learning automata: an introduction. 2012: Courier Corporation.
[67] Thathachar, M.A. and P.S. Sastry, Varieties of learning automata: an overview. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2002. 32(6): p. 711-722.
[68] Hong, T.-P., et al., Genetic-fuzzy data mining with divide-and-conquer strategy. IEEE Transactions on Evolutionary Computation, 2008. 12(2): p. 252-265.
[69] Tao, Y.-H., et al., A practical extension of web usage mining with intentional browsing data toward usage. Expert Systems with Applications, 2009. 36(2): p. 3937-3945.