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Journal articles on the topic 'Fuzzy temporal association rule'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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Matthews, Stephen G., Mario A. Gongora, Adrian A. Hopgood, and Samad Ahmadi. "Web usage mining with evolutionary extraction of temporal fuzzy association rules." Knowledge-Based Systems 54 (December 2013): 66–72. http://dx.doi.org/10.1016/j.knosys.2013.09.003.

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12

Steinbrecher, Matthias, and Rudolf Kruse. "Visualizing and fuzzy filtering for discovering temporal trajectories of association rules." Journal of Computer and System Sciences 76, no. 1 (2010): 77–87. http://dx.doi.org/10.1016/j.jcss.2009.05.007.

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13

Chen, Chun-Hao, Hsiang Chou, Tzung-Pei Hong, and Yusuke Nojima. "Cluster-Based Membership Function Acquisition Approaches for Mining Fuzzy Temporal Association Rules." IEEE Access 8 (2020): 123996–4006. http://dx.doi.org/10.1109/access.2020.3004095.

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14

Zhu, Aihua, Haote Zhang, Xingqian Chen, and Dingkun Zhu. "Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data." Mathematics 13, no. 8 (2025): 1349. https://doi.org/10.3390/math13081349.

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This paper introduces a dual-strategy model based on temporal transformation and fuzzy theory, and designs a partitioned mining algorithm for periodic frequent patterns in large-scale event data (3P-TFT). The model reconstructs original event data through temporal reorganization and attribute fuzzification, preserving data continuity distribution characteristics while enabling efficient processing of multidimensional attributes within a multi-temporal granularity calendar framework. The 3P-TFT algorithm employs temporal interval and object attribute partitioning strategies to achieve distribut

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Murray, James. "AN ASSESSMENT OF FUZZY TEMPORAL EVENT CORRELATION TOWARDS CYBER CRIME INVESTIGATION." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 9, no. 2 (2021): 10–14. http://dx.doi.org/10.55083/irjeas.2021.v09i02006.

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Event logging and event logs play an important role in modern IT systems criminal investigation which is generated when end user with each other in web environment and stored in various logs like firewall log file at side, network log file at gateway and web log file at server side. But log file is not to be over emphasized as a source of information in systems and network management. Whereas conduct efficient investigation and gathering of use full information need to correlate different log file. Task of analysing event log files with the ever-increasing size and complexity of today’s event

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Keskin, Sinan, and Adnan Yazıcı. "Modeling and Querying Fuzzy SOLAP-Based Framework." ISPRS International Journal of Geo-Information 11, no. 3 (2022): 191. http://dx.doi.org/10.3390/ijgi11030191.

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Nowadays, with the rise of sensor technology, the amount of spatial and temporal data is increasing day by day. Modeling data in a structured way and performing effective and efficient complex queries has become more essential than ever. Online analytical processing (OLAP), developed for this purpose, provides appropriate data structures and supports querying multidimensional numeric and alphanumeric data. However, uncertainty and fuzziness are inherent in the data in many complex database applications, especially in spatiotemporal database applications. Therefore, there is always a need to su

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Shao, Jun, Fan Zhang, Chuanzhi Chen, Ye Wang, Qiang Wang, and Jie Zhou. "Brain Network for Exploring the Change of Brain Neurotransmitter 5-Hydroxytryptamine of Autism Children by Resting-State EEG." Computational and Mathematical Methods in Medicine 2022 (April 23, 2022): 1–8. http://dx.doi.org/10.1155/2022/5451277.

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The study was aimed at understanding the brain network and the change rule of brain neurotransmitter 5-hydroxytryptamine (5-HT) in autism children through resting-state electroencephalogram (EEG). 20 autistic children in hospital were selected and defined as the observation group. Meanwhile, 20 healthy children were defined as the control group. EEG signals were collected for the two groups. Fuzzy C-means (FCM) algorithm was used to extract features of EEG signals, and DTF was applied for the causal association between multichannel EEG signals. The two groups were compared for the average func

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18

Lekha, A., C. V. Srikrishna, and Viji Vinod. "Fuzzy Association Rule Mining." Journal of Computer Science 11, no. 1 (2015): 71–74. http://dx.doi.org/10.3844/jcssp.2015.71.74.

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19

Pardeshi, Pramod, and Ujwala Patil. "Fuzzy Association Rule Mining- A Survey." International Journal of Scientific Research in Computer Science and Engineering 5, no. 6 (2017): 13–18. http://dx.doi.org/10.26438/ijsrcse/v5i6.1318.

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PACH, F., A. GYENESEI, and J. ABONYI. "Compact fuzzy association rule-based classifier." Expert Systems with Applications 34, no. 4 (2008): 2406–16. http://dx.doi.org/10.1016/j.eswa.2007.04.005.

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21

KORPIPÄÄ, PANU. "Visualizing constraint-based temporal association rules." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 5 (2001): 401–10. http://dx.doi.org/10.1017/s0890060401155034.

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When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and gly

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22

Rajkamal Sarma. "Discovery of Fuzzy and Composite Fuzzy Association Rules in Meteorological Data." Journal of Information Systems Engineering and Management 10, no. 37s (2025): 677–97. https://doi.org/10.52783/jisem.v10i37s.6505.

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Fuzzy Association Rule Mining (FARM) extends traditional ARM by evaluating and pruning rules based on interestingness measures to identify relevant patterns for various applications. The focus of this paper is to explore the application of FARM techniques demonstrating its algorithmic implementation in a meteorological dataset. Three major algorithms known as fuzzy Apriori, FTDA (Fuzzy Transaction Data-Mining Algorithm) and CFARM Composite Fuzzy Association Rule Mining) are experimented and analyzed. The experiment uses a real meteorological dataset spanning twenty years consisting some import

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Verma, Keshri, and O. P. Vyas. "Efficient calendar based temporal association rule." ACM SIGMOD Record 34, no. 3 (2005): 63–70. http://dx.doi.org/10.1145/1084805.1084818.

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24

Liu, Xiaoyan, Feng Feng, Qian Wang, Ronald R. Yager, Hamido Fujita, and José Carlos R. Alcantud. "Mining Temporal Association Rules with Temporal Soft Sets." Journal of Mathematics 2021 (November 29, 2021): 1–17. http://dx.doi.org/10.1155/2021/7303720.

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Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q -clip soft sets to establish a novel framework fo

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25

Kim, Mi-Hye. "Intelligent Query Analysis using Fuzzy Association Rule." Journal of the Korea Academia-Industrial cooperation Society 11, no. 6 (2010): 2214–18. http://dx.doi.org/10.5762/kais.2010.11.6.2214.

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26

Kalia, Harihar, Satchidananda Dehuri, and Ashish Ghosh. "A Survey on Fuzzy Association Rule Mining." International Journal of Data Warehousing and Mining 9, no. 1 (2013): 1–27. http://dx.doi.org/10.4018/jdwm.2013010101.

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Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid prob

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A, Anitha, and Freeda Jebamalar.S. "Predicting Dengue Using Fuzzy Association Rule Mining." International Journal of Computer Trends and Technology 67, no. 3 (2019): 72–74. http://dx.doi.org/10.14445/22312803/ijctt-v67i3p114.

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Oh, Kabsuk, and Kaoru Hirota. "Support System for Multimedia Information Data Acquisition Based on Fuzzy Inference with a Fuzzy Shift." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (2000): 387–94. http://dx.doi.org/10.20965/jaciii.2000.p0387.

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Support system construction for multimedia information data acquisition based on fuzzy inference with a concept of fuzzy shift is proposed, where the multimedia means the five senses. Observed information from the outside world is characterized by VAGOT (visual, acoustic, gustatory, olfactory, and tactile) time series data. Here, multimedia information centers on image and sound are represented by membership functions. Fuzzy rules based on visual and acoustic information are used to identify the appropriate time interval on multimedia input data. The proposed system is constructed by rule cons

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Sonia M, Delphin, John Robinson P, and Sebastian Rajasekaran A. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills." International Journal of Agricultural and Environmental Information Systems 6, no. 2 (2015): 40–76. http://dx.doi.org/10.4018/ijaeis.2015040104.

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The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regress

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Thomas, Binu, and G. Raju. "A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules." ISRN Artificial Intelligence 2013 (December 19, 2013): 1–10. http://dx.doi.org/10.1155/2013/316913.

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In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transact

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Mittal, Mandeep, Sarla Pareek, and Reshu Agarwal. "Ordering policy using temporal association rule mining." International Journal of Data Science 1, no. 2 (2015): 157. http://dx.doi.org/10.1504/ijds.2015.072419.

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Bai, Yi Ming, Xian Yao Meng, and Xin Jie Han. "Mining Fuzzy Association Rules in Quantitative Databases." Applied Mechanics and Materials 182-183 (June 2012): 2003–7. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.2003.

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In this paper, we introduce a novel technique for mining fuzzy association rules in quantitative databases. Unlike other data mining techniques who can only discover association rules in discrete values, the algorithm reveals the relationships among different quantitative values by traversing through the partition grids and produces the corresponding Fuzzy Association Rules. Fuzzy Association Rules employs linguistic terms to represent the revealed regularities and exceptions in quantitative databases. After the fuzzy rule base is built, we utilize the definition of Support Degree in data mini

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Di Martino, Ferdinando, and Salvatore Sessa. "Detection of Fuzzy Association Rules by Fuzzy Transforms." Advances in Fuzzy Systems 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/258476.

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We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform. Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show tha

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Roy, Aritra. "A Survey on Fuzzy Association Rule Mining Methodologies." IOSR Journal of Computer Engineering 15, no. 6 (2013): 01–08. http://dx.doi.org/10.9790/0661-1560108.

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Chaturvedi, Kapil, Dr Ravindra Patel, and Dr D. K. Swami. "A Fuzzy Inference Approach for Association Rule Mining." IOSR Journal of Computer Engineering 16, no. 6 (2014): 57–66. http://dx.doi.org/10.9790/0661-16615766.

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Veeramalai, Sankaradass, and Arputharaj Kannan. "Intelligent Information Retrieval Using Fuzzy Association Rule Classifier." International Journal of Intelligent Information Technologies 7, no. 3 (2011): 14–27. http://dx.doi.org/10.4018/jiit.2011070102.

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As the use of web applications increases, when users use search engines for finding some information by inputting keywords, the number of web pages that match the information increases at a tremendous rate. It is not easy for a user to retrieve the exact web page which contains information he or she requires. In this paper, an approach to web page retrieval system using the hybrid combination of context based and collaborative filtering method employing the concept of fuzzy association rule classification is introduced and the authors propose an innovative clustering of user profiles in order

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Srivastava, Deepesh Kumar, Basav Roychoudhury, and Harsh Vardhan Samalia. "Fuzzy association rule mining for economic development indicators." International Journal of Intelligent Enterprise 6, no. 1 (2019): 3. http://dx.doi.org/10.1504/ijie.2019.100030.

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Srivastava, Deepesh Kumar, Harsh Vardhan Samalia, and Basav Roychoudhury. "Fuzzy association rule mining for economic development indicators." International Journal of Intelligent Enterprise 6, no. 1 (2019): 3. http://dx.doi.org/10.1504/ijie.2019.10021610.

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Lee, Carmen Kar Hang, Y. K. Tse, G. T. S. Ho, and K. L. Choy. "Fuzzy association rule mining for fashion product development." Industrial Management & Data Systems 115, no. 2 (2015): 383–99. http://dx.doi.org/10.1108/imds-09-2014-0277.

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Purpose – The emergence of the fast fashion trend has exerted a great pressure on fashion designers who are urged to consider customers’ preferences in their designs and develop new products in an efficient manner. The purpose of this paper is to develop a fuzzy association rule mining (FARM) approach for improving the efficiency and effectiveness of new product development (NPD) in fast fashion. Design/methodology/approach – The FARM identifies the hidden relationships between product styles and customer preferences. The knowledge discovered help the fashion industry design new products which

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Zheng, Hui, Jing He, Guangyan Huang, Yanchun Zhang, and Hua Wang. "Dynamic optimisation based fuzzy association rule mining method." International Journal of Machine Learning and Cybernetics 10, no. 8 (2018): 2187–98. http://dx.doi.org/10.1007/s13042-018-0806-9.

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Mahmoodian, Hamid, M. Hamiruce Marhaban, Raha Abdulrahim, Rozita Rosli, and Iqbal Saripan. "Using fuzzy association rule mining in cancer classification." Australasian Physical & Engineering Sciences in Medicine 34, no. 1 (2011): 41–54. http://dx.doi.org/10.1007/s13246-011-0054-8.

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Salman, Nur, Mustikasari Mustikasari, and Muhammad Nur Akbar. "Penambangan Pengklasifiksi Fuzzy dengan Multiobjective Evolutionary Fuzzy Classifier." Journal Software, Hardware and Information Technology 2, no. 1 (2022): 59–65. http://dx.doi.org/10.24252/shift.v2i1.24.

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Classification is one of the key issues in the field of data mining and knowledge discovery. This paper implements a method of constructing a fuzzy rule mining classifier, which is extended in the context of classification. There are three stages of this approach: fuzzy rule set extraction, second; a linguistic labeling process that assigns a linguistic label to each fuzzy set. Owing to many attributes in the database, the feature selection process is also carried out, reducing the complexity to build the final classifier. Third: incorporate strategies to avoid rule redundancy and conflict int

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Oladipupo, Olufunke O., Charles O. Uwadia, and Charles K. Ayo. "Improving medical rule-based expert systems comprehensibility: fuzzy association rule mining approach." International Journal of Artificial Intelligence and Soft Computing 3, no. 1 (2012): 29. http://dx.doi.org/10.1504/ijaisc.2012.048179.

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Agarwal, Reshu, Mandeep Mittal, and Sarla Pareek. "Loss Profit Estimation Using Temporal Association Rule Mining." International Journal of Business Analytics 3, no. 1 (2016): 45–57. http://dx.doi.org/10.4018/ijban.2016010103.

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Temporal association rule mining is a data mining technique in which relationships between items which satisfy certain timing constraints can be discovered. This paper presents the concept of temporal association rules in order to solve the problem of classification of inventories by including time expressions into association rules. Firstly, loss profit of frequent items is calculated by using temporal association rule mining algorithm. Then, the frequent items in particular time-periods are ranked according to descending order of loss profits. The manager can easily recognize most profitable

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Agarwal, Reshu. "Opportunity cost estimation using temporal association rule mining." International Journal of Services Sciences 6, no. 3/4 (2017): 261. http://dx.doi.org/10.1504/ijssci.2017.091819.

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Agarwal, Reshu. "Opportunity cost estimation using temporal association rule mining." International Journal of Services Sciences 6, no. 3/4 (2017): 261. http://dx.doi.org/10.1504/ijssci.2017.10013059.

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Abouzakhar, Nasser S., Huankai Chen, and Bruce Christianson. "An Enhanced Fuzzy ARM Approach for Intrusion Detection." International Journal of Digital Crime and Forensics 3, no. 2 (2011): 41–61. http://dx.doi.org/10.4018/jdcf.2011040104.

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The integration of fuzzy logic with data mining methods such as association rules has achieved interesting results in various digital forensics applications. As a data mining technique, the association rule mining (ARM) algorithm uses ranges to convert any quantitative features into categorical ones. Such features lead to the sudden boundary problem, which can be smoothed by incorporating fuzzy logic so as to develop interesting patterns for intrusion detection. This paper introduces a Fuzzy ARM-based intrusion detection model that is tested on the CAIDA 2007 backscatter network traffic datase

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48

Mucientes, M. "A fuzzy temporal rule-based velocity controller for mobile robotics." Fuzzy Sets and Systems 134, no. 1 (2003): 83–99. http://dx.doi.org/10.1016/s0165-0114(02)00231-2.

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Li, Tianyu, Fangyan Dong, and Kaoru Hirota. "Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (2015): 217–24. http://dx.doi.org/10.20965/jaciii.2015.p0217.

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A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.

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Carse, Brian, Terence C. Fogarty, and Alistair Munro. "Artificial evolution of fuzzy rule bases which represent time: A temporal fuzzy classifier system." International Journal of Intelligent Systems 13, no. 10-11 (1998): 905–27. http://dx.doi.org/10.1002/(sici)1098-111x(199810/11)13:10/11<905::aid-int3>3.0.co;2-2.

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