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1

Szathmáry, László. "Closed Association Rules." Annales Mathematicae et Informaticae 51 (2020): 65–76. http://dx.doi.org/10.33039/ami.2020.07.009.

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2

Abdullah, Zailani, Tutut Herawan, and Mustafa Mat Deris. "Tracing significant association rules using critical least association rules model." International Journal of Innovative Computing and Applications 5, no. 1 (2013): 3. http://dx.doi.org/10.1504/ijica.2013.052350.

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3

Lu, Songfeng, Heping Hu, and Fan Li. "Mining weighted association rules." Intelligent Data Analysis 5, no. 3 (2001): 211–25. http://dx.doi.org/10.3233/ida-2001-5303.

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4

Defit, Sarjon. "Intelligent Mining Association Rules." International Journal of Computer Science and Information Technology 4, no. 4 (2012): 97–106. http://dx.doi.org/10.5121/ijcsit.2012.4409.

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5

Srikant, Ramakrishnan, and Rakesh Agrawal. "Mining generalized association rules." Future Generation Computer Systems 13, no. 2-3 (1997): 161–80. http://dx.doi.org/10.1016/s0167-739x(97)00019-8.

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6

Zhou, Aoying, Shuigeng Zhou, Wen Jin, and Zengping Tian. "Generalized multidimensional association rules." Journal of Computer Science and Technology 15, no. 4 (2000): 388–92. http://dx.doi.org/10.1007/bf02948876.

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7

Zhan, Foxiao, Xiaolan Zhu, Lei Zhang, Xuexi Wang, Lu Wang, and Chaoyi Liu. "Summary of Association Rules." IOP Conference Series: Earth and Environmental Science 252 (July 9, 2019): 032219. http://dx.doi.org/10.1088/1755-1315/252/3/032219.

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8

Mani, Tushar. "Mining Negative Association Rules." IOSR Journal of Computer Engineering 3, no. 6 (2012): 43–47. http://dx.doi.org/10.9790/0661-0364347.

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9

Rauch, Jan. "Logic of Association Rules." Applied Intelligence 22, no. 1 (2005): 9–28. http://dx.doi.org/10.1023/b:apin.0000047380.15356.7a.

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10

Tzoulis, A., and T. F. Eibert. "Fast computation of electromagnetic near-fields with the multilevel fast multipole method combining near-field and far-field translations." Advances in Radio Science 4 (September 4, 2006): 111–15. http://dx.doi.org/10.5194/ars-4-111-2006.

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Abstract. In Electromagnetic Compatibility (EMC) problems, computation of electromagnetic near-fields in the vicinity of complex radiation and scattering systems is often required. Numerical solution of such problems is achieved using Boundary Integral (BI) based approaches, where the involved Integral Equations (IE's) are solved with the Method of Moments (MoM). The MoM solution process is speeded up by fast IE solvers such as the Multilevel Fast Multipole Method (MLFMM). In the end the desired amplitudes of the expansion of the equivalent current densities on the discrete elements all over t
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11

Krause, John M. "A Simple Algorithm to Discriminate between Meteorological and Nonmeteorological Radar Echoes." Journal of Atmospheric and Oceanic Technology 33, no. 9 (2016): 1875–85. http://dx.doi.org/10.1175/jtech-d-15-0239.1.

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AbstractDiscriminating between meteorological and nonmeteorological radar returns is necessary for a number of radar applications, including hydrometeor classification, quantitative precipitation estimation (QPE), and the computation of specific differential phase KDP. The algorithm proposed, MetSignal, uses polarimetric radar data and is simple by design, allowing users to adjust its performance based on the location’s specific needs. The MetSignal algorithm is a fuzzy logic technique with a few postprocessing rules and has been selected for implementation on the WSR-88D network in the United
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12

Liu, Fang, Zhengding Lu, and Songfeng Lu. "Mining association rules using clustering." Intelligent Data Analysis 5, no. 4 (2001): 309–26. http://dx.doi.org/10.3233/ida-2001-5403.

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13

Yokobayashi, Ryohei, and Takao Miura. "Multidimensional Association Rules on Tensors." International Journal of Computational Linguistics Research 9, no. 2 (2018): 106. http://dx.doi.org/10.6025/jcl/2018/9/2/106-119.

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14

Taha, Mohamed, Tarek F. Gharib, and Hamed Nassar. "DARM: Decremental Association Rules Mining." Journal of Intelligent Learning Systems and Applications 03, no. 03 (2011): 181–89. http://dx.doi.org/10.4236/jilsa.2011.33019.

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15

Tan, Jun, and Ying Yong Bu. "Association Rules Mining in Manufacturing." Applied Mechanics and Materials 34-35 (October 2010): 651–54. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.651.

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In recent years, manufacturing processes have become more and more complex, manufacturing activities generate large quantities of data, so it is no longer practical to rely on traditional manual methods to analyze this data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining techniques and has received considerable attention from researchers and practitioners. The paper presents the basic concept of association rule mining and reviews applications of as
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16

Dogan, Sengul, and Ibrahim Turkoglu. "Diagnosing Hyperlipidemia Using Association Rules." Mathematical and Computational Applications 13, no. 3 (2008): 193–202. http://dx.doi.org/10.3390/mca13030193.

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17

Mazouri, Fatima-Zahra El, Said Jabbour, Badran Raddaoui, Lakhdar Sais, Mohammed Chaouki Abounaima, and Khalid Zenkouar. "Breaking Symmetries in Association Rules." Procedia Computer Science 148 (2019): 283–90. http://dx.doi.org/10.1016/j.procs.2019.01.052.

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18

Raval, Rina. "F3 Algorithm for Association Rules." International Journal of Computer Applications 164, no. 10 (2017): 6–11. http://dx.doi.org/10.5120/ijca2017913690.

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19

Fan, Wenfei, Xin Wang, Yinghui Wu, and Jingbo Xu. "Association rules with graph patterns." Proceedings of the VLDB Endowment 8, no. 12 (2015): 1502–13. http://dx.doi.org/10.14778/2824032.2824048.

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20

Nanopoulos, Alexandros, and Yannis Manolopoulos. "Memory-adaptive association rules mining." Information Systems 29, no. 5 (2004): 365–84. http://dx.doi.org/10.1016/s0306-4379(03)00035-8.

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21

Taniar, David, Wenny Rahayu, Olena Daly, and Hong-Quang Nguyen. "Mining Hierarchical Negative Association Rules." International Journal of Computational Intelligence Systems 5, no. 3 (2012): 434–51. http://dx.doi.org/10.1080/18756891.2012.696905.

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22

Miller, R. J., and Y. Yang. "Association rules over interval data." ACM SIGMOD Record 26, no. 2 (1997): 452–61. http://dx.doi.org/10.1145/253262.253361.

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23

Zaki, Mohammed J. "Mining Non-Redundant Association Rules." Data Mining and Knowledge Discovery 9, no. 3 (2004): 223–48. http://dx.doi.org/10.1023/b:dami.0000040429.96086.c7.

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24

Subramanyam, R. B. V., and A. Goswami. "Mining fuzzy quantitative association rules." Expert Systems 23, no. 4 (2006): 212–25. http://dx.doi.org/10.1111/j.1468-0394.2006.00402.x.

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25

Veselý, Arnošt. "INFORMATION CONTENT OF ASSOCIATION RULES." Neural Network World 24, no. 3 (2014): 231–48. http://dx.doi.org/10.14311/nnw.2014.24.014.

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26

Agrawal, R., and J. C. Shafer. "Parallel mining of association rules." IEEE Transactions on Knowledge and Data Engineering 8, no. 6 (1996): 962–69. http://dx.doi.org/10.1109/69.553164.

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27

Ashrafi, Mafruz Zaman, David Taniar, and Kate Smith. "Redundant association rules reduction techniques." International Journal of Business Intelligence and Data Mining 2, no. 1 (2007): 29. http://dx.doi.org/10.1504/ijbidm.2007.012945.

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28

Wang, Shyue-Liang, Dipen Patel, Ayat Jafari, and Tzung-Pei Hong. "Hiding collaborative recommendation association rules." Applied Intelligence 27, no. 1 (2007): 67–77. http://dx.doi.org/10.1007/s10489-006-0031-1.

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29

Hofmann, Heike, and Adalbert Wilhelm. "Visual Comparison of Association Rules." Computational Statistics 16, no. 3 (2001): 399–415. http://dx.doi.org/10.1007/s001800100075.

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30

Lee, Wan-Jui, Jung-Yi Jiang, and Shie-Jue Lee. "Mining fuzzy periodic association rules." Data & Knowledge Engineering 65, no. 3 (2008): 442–62. http://dx.doi.org/10.1016/j.datak.2007.11.002.

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31

Xu, Yue, Yuefeng Li, and Gavin Shaw. "Reliable representations for association rules." Data & Knowledge Engineering 70, no. 6 (2011): 555–75. http://dx.doi.org/10.1016/j.datak.2011.02.003.

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32

Chiang, Ding-An, Yi-Fan Wang, Yi-Hsin Wang, Zhi-Yang Chen, and Mei-Hua Hsu. "Mining disjunctive consequent association rules." Applied Soft Computing 11, no. 2 (2011): 2129–33. http://dx.doi.org/10.1016/j.asoc.2010.07.011.

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33

Tackett, James A. "Association Rules for Fraud Detection." Journal of Corporate Accounting & Finance 24, no. 4 (2013): 15–22. http://dx.doi.org/10.1002/jcaf.21856.

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34

Cagliero, Luca, and Alessandro Fiori. "Discovering generalized association rules from Twitter." Intelligent Data Analysis 17, no. 4 (2013): 627–48. http://dx.doi.org/10.3233/ida-130597.

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35

Tjioe, Haorianto Cokrowijoyo, and David Taniar. "Mining Association Rules in Data Warehouses." International Journal of Data Warehousing and Mining 1, no. 3 (2005): 28–62. http://dx.doi.org/10.4018/jdwm.2005070103.

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36

Kumari, Reena, and Jyoti Vashishtha. "Discovery of Fuzzy Hierarchical Association Rules." International Journal of Computer Applications 98, no. 19 (2014): 20–26. http://dx.doi.org/10.5120/17292-7762.

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37

Hong, Tzung-Pei, Chan-Sheng Kuo, and Sheng-Chai Chi. "Mining association rules from quantitative data☆." Intelligent Data Analysis 3, no. 5 (1999): 363–76. http://dx.doi.org/10.3233/ida-1999-3504.

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38

Malik, Waqas Ahmed, and Antony Unwin. "Automated error detection using association rules." Intelligent Data Analysis 15, no. 5 (2011): 749–61. http://dx.doi.org/10.3233/ida-2011-0493.

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39

Dong, Liyan, Renbiao Wang, and Yongli Li. "Mining Association Rules Based on Certainty." International Journal of Intelligent Engineering and Systems 5, no. 3 (2012): 19–27. http://dx.doi.org/10.22266/ijies2012.9030.03.

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40

Huang, Yin-Fu, and Chieh-Ming Wu. "Preknowledge-based generalized association rules mining." Journal of Intelligent & Fuzzy Systems 22, no. 1 (2011): 1–13. http://dx.doi.org/10.3233/ifs-2010-0469.

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41

kar, Dr.A.Ramamohanreddy, B. Ramasubbareddy, K. Se. "Algorithm to Mine Negative Association Rules." International Journal of Innovative Research in Science, Engineering and Technology 1, no. 1 (2012): 121–26. http://dx.doi.org/10.15680/ijirset.2012.0101014.

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42

Jiang, He, Ai Xin Yang, and Hong Jun Yu. "Study on Multidimensional Negative Association Rules." Applied Mechanics and Materials 644-650 (September 2014): 1721–24. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1721.

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With the deepening of the negative association rules mining technology research, many key problems have been solved, but the solution of these problems are all on a single predicate in the transaction database. However, the data in the database often involves multiple predicates. This paper focuses on solving multi-dimensional support and confidence, negative association rules mining algorithm design problems. The experiment proves that the algorithm is correct and efficiency.
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43

Wang, Hui. "Hiding Sensitive Association Rules by Sanitizing." Advanced Materials Research 694-697 (May 2013): 2317–21. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2317.

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The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.
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44

Tan, Jun. "Weighted Association Rules Mining Algorithm Research." Applied Mechanics and Materials 241-244 (December 2012): 1598–601. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1598.

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Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.
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45

Pandey, Sachin. "Multilevel Association Rules in Data Mining." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (2018): 74–78. http://dx.doi.org/10.29070/15/57517.

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46

Jabbour, Said, Fatima Ezzahra El Mazouri, and Lakhdar Sais. "Mining Negatives Association Rules Using Constraints." Procedia Computer Science 127 (2018): 481–88. http://dx.doi.org/10.1016/j.procs.2018.01.146.

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47

U., Deepa, and Nilam K. "Mining Association Rules using R Environment." International Journal of Computer Applications 157, no. 4 (2017): 45–50. http://dx.doi.org/10.5120/ijca2017912679.

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48

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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49

Guang-yuan, Li, Cao Dan-yang, and Guo Jian-wei. "Association Rules Mining with Multiple Constraints." Procedia Engineering 15 (2011): 1678–83. http://dx.doi.org/10.1016/j.proeng.2011.08.313.

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50

Li, Yingjiu, Peng Ning, X. Sean Wang, and Sushil Jajodia. "Discovering calendar-based temporal association rules." Data & Knowledge Engineering 44, no. 2 (2003): 193–218. http://dx.doi.org/10.1016/s0169-023x(02)00135-0.

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