Relevant bibliographies by topics / Fuzzy temporal association rule

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  2. Dissertations / Theses
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Academic literature on the topic 'Fuzzy temporal association rule'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Fuzzy temporal association rule"

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Nupur, Bhagoriya* Deepak Agrawal Zeba Qureshi. "TEMPORAL ASSOCIATION RULE MINING: A SURVEY IN FUZZY FRAMEWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 706–9. https://doi.org/10.5281/zenodo.569946.

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Temporal data mining generate temporal association rule that encapsulate transaction of item with time that’s recorded in temporal data base. Now these days recent research has focused to generate efficient fuzzy temporal association rule and transforming each quantitative value into fuzzy sets using the given membership functions. This paper presents a survey on temporal association rule and fuzzy logic. The Technical constraint of temporal data mining and fuzzy logic are identified and presented.
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Li, Zhi Gang, and Feng Li Yang. "The Generation of the Fuzzy Control Rules Based on Association Rules with Temporal Constraints." Applied Mechanics and Materials 385-386 (August 2013): 931–34. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.931.

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In the field of fuzzy control, the generation of fuzzy control rules has always been a problem, because the industrial data is generally expressed in the order of time ,so it strongly depends on the time, it does not take the factors of temporal constraints into account in the previous extracting rule process.This paper uses temporal constraint association rule ,and uses the data mining methods to generate temporal fuzzy control rules. The method is verified by using the MATLAB7.1 ,the simulation shows that the method can achieve good fuzzy control rules.
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Matthews, Stephen G., Mario A. Gongora, and Adrian A. Hopgood. "Evolutionary algorithms and fuzzy sets for discovering temporal rules." International Journal of Applied Mathematics and Computer Science 23, no. 4 (2013): 855–68. http://dx.doi.org/10.2478/amcs-2013-0064.

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Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown t
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Cariñena, Purificación. "Fuzzy temporal association rules: combining temporal and quantitative data to increase rule expressiveness." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 1 (2013): 64–70. http://dx.doi.org/10.1002/widm.1116.

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Zhu, Aihua, Zhiqing Meng, and Rui Shen. "Research on Fuzzy Temporal Event Association Mining Model and Algorithm." Axioms 12, no. 2 (2023): 117. http://dx.doi.org/10.3390/axioms12020117.

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As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving th
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B, Likhith. "Web Personalized Recommendation Model Using Temporal Fuzzy Association Rule Mining." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 1831–41. http://dx.doi.org/10.22214/ijraset.2022.45559.

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Abstract: A web access log file contains timely sequenced log entries which include essential fields to indicate user activities. Analysis of these patterns provides valuable information for web designer to quickly respond to their individual needs. Many industries are struggling to retain regular interested customers for the improvement of customer relationship. Retrieval of relevant information automatically from these log files for interested group of users is a difficult process, since acquiring interested user profiles which evolves continuously with respect to time are not so easy. The p
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Lee, W. J., and S. J. Lee. "Discovery of Fuzzy Temporal Association Rules." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 6 (2004): 2330–42. http://dx.doi.org/10.1109/tsmcb.2004.835352.

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Rouza, Erni, M. Riski Alfares, Riri Anjeli, Bayu Ramadhan Azhari, and Veldy Harnanda. "Sistem Pakar Diagnosa Penyakit Pada Kelinci Dengan Menggunakan Metode Fuzzy Temporal Association Rule." RJOCS (Riau Journal of Computer Science) 8, no. 01 (2022): 56–66. http://dx.doi.org/10.30606/rjocs.v8i01.1191.

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Kelinci merupakan hewan mamalia yang dapat ditemukan dibanyak bagian belahan bumi.Saat ini sejumlah jenis kelinci menjadi hewan peliharaan dan hewan pedaging. Populasi kelinci sudah mulai banyak yang menjadikan hewan ternak kemudian ada juga yang minat akan budi daya kelinci karena kelinci merupakan hewan yang dapat dengan mudah berkembang biak, oleh karena itu para peternak harus semakin berhati-hati akan Kesehatan hewan peliharaan tersebut, karena kelinci sama seperti hewan ternak lainnya yang memiliki banyak jenis penyakit. Aplikasi system pakar diagnose penyakit pada kelinci menggunakan me
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Chen, Chun-Hao, Guo-Cheng Lan, Tzung-Pei Hong, and Shih-Bin Lin. "Mining fuzzy temporal association rules by item lifespans." Applied Soft Computing 41 (April 2016): 265–74. http://dx.doi.org/10.1016/j.asoc.2016.01.008.

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Oluwafemi, Oriola, B. Adeyemo Adesesan, and Osunade Oluwaseyitanfunmi. "Network Threat Characterization in Multiple Intrusion Perspectives using Data Mining Technique." International Journal of Network Security & Its Applications (IJNSA) 4, no. 6 (2012): 145–56. https://doi.org/10.5281/zenodo.3714462.

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For effective security incidence response on the network, a reputable approach must be in place at both protected and unprotected region of the network. This is because compromise in the demilitarized zone could be precursor to threat inside the network. The improved complexity of attacks in present times and vulnerability of system are motivations for this work. Past and present approaches to intrusion detection and prevention have neglected victim and attacker properties despite the fact that for intrusion to occur, an overt act by an attacker and a manifestation, observable by the intended
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Dissertations / Theses on the topic "Fuzzy temporal association rule"

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Matthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.

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Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are ina
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Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.

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Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since
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Carse, Brian. "Artificial evolution of fuzzy and temporal rule based systems." Thesis, University of the West of England, Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267551.

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Weitl, Harms Sherri K. "Temporal association rule methodologies for geo-spatial decision support /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3091989.

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Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.

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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.<br>Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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Isik, Narin. "Fuzzy Spatial Data Cube Construction And Its Use In Association Rule Mining." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606056/index.pdf.

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The popularity of spatial databases increases since the amount of the spatial data that need to be handled has increased by the use of digital maps, images from satellites, video cameras, medical equipment, sensor networks, etc. Spatial data are difficult to examine and extract interesting knowledge<br>hence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowledge from spatial data is generated by construction of fuzzy spatial data cube and extr
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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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He, Yuanchen. "Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/12.

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Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical dom
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Castro, Ricardo Ferreira Vieira de. "Análise de desempenho dos algoritmos Apriori e Fuzzy Apriori na extração de regras de associação aplicados a um Sistema de Detecção de Intrusos." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8137.

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A extração de regras de associação (ARM - Association Rule Mining) de dados quantitativos tem sido pesquisa de grande interesse na área de mineração de dados. Com o crescente aumento das bases de dados, há um grande investimento na área de pesquisa na criação de algoritmos para melhorar o desempenho relacionado a quantidade de regras, sua relevância e a performance computacional. O algoritmo APRIORI, tradicionalmente usado na extração de regras de associação, foi criado originalmente para trabalhar com atributos categóricos. Geralmente, para usá-lo com atributos contínuos, ou quantitativos, é
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Sammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.

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De nos jours, afin de répondre aux exigences économiques et sociales, les systèmes de transport ferroviaire ont la nécessité d'être exploités avec un haut niveau de sécurité et de fiabilité. On constate notamment un besoin croissant en termes d'outils de surveillance et d'aide à la maintenance de manière à anticiper les défaillances des composants du matériel roulant ferroviaire. Pour mettre au point de tels outils, les trains commerciaux sont équipés de capteurs intelligents envoyant des informations en temps réel sur l'état de divers sous-systèmes. Ces informations se présentent sous la form
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Book chapters on the topic "Fuzzy temporal association rule"

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Matthews, Stephen G., Mario A. Gongora, and Adrian A. Hopgood. "Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21219-2_26.

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Tan, Ting-Feng, Qing-Guo Wang, Tian-He Phang, Xian Li, Jiangshuai Huang, and Dan Zhang. "Temporal Association Rule Mining." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_24.

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Sarma, Rajkamal, and Pankaj Kumar Deva Sarma. "Fuzzy Association Rule Mining Techniques and Applications." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47224-4_7.

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Wahl, Scott, and John Sheppard. "Association Rule Mining in Fuzzy Political Donor Communities." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96133-0_18.

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Pach, F. P., A. Gyenesei, P. Arval, and J. Abonyi. "Fuzzy Association Rule Mining for Model Structure Identification." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36266-1_25.

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Rusnok, Pavel, and Michal Burda. "Global Quality Measures for Fuzzy Association Rule Bases." In Advances in Fuzzy Logic and Technology 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66827-7_24.

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Farrokhizadeh, Elmira, and Basar Oztaysi. "A Novel Hesitant Fuzzy Association Rule Mining Model." In Lecture Notes in Management and Industrial Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25847-3_4.

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Isik, Narin, and Adnan Yazici. "Association Rule Mining using Fuzzy Spatial Data Cubes." In Geographic Uncertainty in Environmental Security. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6438-8_12.

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Cariñena, Purificación, Alberto Bugarín, Manuel Mucientes, Félix Díaz-Hermida, and Senén Barro. "Fuzzy Temporal Rules: A Rule-based Approach for Fuzzy Temporal Knowledge Representation and Reasoning." In Technologies for Constructing Intelligent Systems 2. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1796-6_19.

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Dockhorn, Alexander, Chris Saxton, and Rudolf Kruse. "Association Rule Mining for Unknown Video Games." In Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54341-9_22.

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Conference papers on the topic "Fuzzy temporal association rule"

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Cao, Yang, and Pengzhi Ma. "Framework for Fuzzy Temporal Association Rule Mining Based on Density Clustering Optimization." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743958.

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Heidari Iman, Mohammad Reza, Gert Jervan, and Tara Ghasempouri. "ARTmine: Automatic Association Rule Mining with Temporal Behavior for Hardware Verification." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546742.

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Huang, Rikui, Wei Wei, Xiaoye Qu, Shengzhe Zhang, Dangyang Chen, and Yu Cheng. "Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.580.

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M, Yadhveer R., and Geetha Mary Amalanathan. "Privacy Preservation in Quantitative Association Rule Mining Using Fuzzy Logic-Based Sanitization." In 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2024. https://doi.org/10.1109/pdgc64653.2024.10983944.

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Matthews, Stephen G., Mario A. Gongora, Adrian A. Hopgood, and Samad Ahmadi. "Temporal fuzzy association rule mining with 2-tuple linguistic representation." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251173.

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Li, Zebang, Fan Bu, and Fusheng Yu. "Temporal fuzzy association rules mining based on fuzzy information granulation." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8392930.

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Shu, Hong, Lin Dong, and Xinyan Zhu. "Mining fuzzy association rules in spatio-temporal databases." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815993.

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Steinbrecher, Matthias, and Rudolf Kruse. "Identifying temporal trajectories of association rules with fuzzy descriptions." In NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2008. http://dx.doi.org/10.1109/nafips.2008.4531243.

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Miao, Ru, and Xia-Jiong Shen. "Construction of periodic temporal association rules in data mining." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569736.

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Moujabbir, Mohammed, and Mohammed Ramdani. "Fuzzy Galois connections for the extraction of temporal association rules." In 2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2013. http://dx.doi.org/10.1109/sita.2013.6560803.

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