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Journal articles on the topic 'Rules of association'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Prakash, R. Vijaya, S. S. V. N. Sarma, and M. Sheshikala. "Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4568–76. https://doi.org/10.11591/ijece.v8i6.pp4568-4576.

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Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given n

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2

Ali, Nzar Abdulqader. "Finding minimum confidence threshold to avoid derived rules in association rule minin." Journal of Zankoy Sulaimani - Part A 17, no. 4 (2015): 271–78. http://dx.doi.org/10.17656/jzs.10443.

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3

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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4

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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5

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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6

Kazienko, Przemysław. "Mining Indirect Association Rules for Web Recommendation." International Journal of Applied Mathematics and Computer Science 19, no. 1 (2009): 165–86. http://dx.doi.org/10.2478/v10006-009-0015-5.

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Mining Indirect Association Rules for Web RecommendationClassical association rules, here called "direct", reflect relationships existing between items that relatively often co-occur in common transactions. In the web domain, items correspond to pages and transactions to user sessions. The main idea of the new approach presented is to discover indirect associations existing between pages that rarely occur together but there are other, "third" pages, called transitive, with which they appear relatively frequently. Two types of indirect associations rules are described in the paper: partial indi

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7

Nguyen, Loan, Ngoc-Thanh Nguyen, and Bogdan Trawiński. "A Quick Method for Querying Top-k Rules from Class Association Rule Set." JUCS - Journal of Universal Computer Science 22, no. (6) (2016): 822–35. https://doi.org/10.3217/jucs-022-06-0822.

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Finding class association rules (CARs) is one of the most important research topics in data mining and knowledge discovery, with numerous applications in many fields. However, existing techniques usually generate an extremely large number of results, which makes analysis difficult. In many applications, experts are interested in only the most relevant results. Therefore, we propose a method for querying top-k CARs based on their supports. From the set of mined CARs that satisfy the minimum support and the minimum confidence thresholds, we use a QuickSort-based method to query top-k rules. The

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8

Ksiksi, Asma, and Hamid Amiri. "Using Association Rules to Enrich Arabic Ontology." Engineering, Technology & Applied Science Research 8, no. 3 (2018): 2914–18. https://doi.org/10.5281/zenodo.1344202.

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In this article, we propose the use of a minimal generic base of associative rules between term association rules, to automatically enrich an existing domain ontology. Initially, nonredundant association rules between terms are extracted from an Arabic corpus. Then, the matching of the candidate terms is done through the matching between the concepts of the initial ontology and the premises of the association rules, with three distance measures that we define.

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9

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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10

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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11

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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12

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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13

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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14

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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15

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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16

Hee Chang, Park. "Generation Method of Association Rules by Symmetric Hellinger Measure." Korean Data Analysis Society 19, no. 5 (2017): 2323–29. http://dx.doi.org/10.37727/jkdas.2017.19.5.2323.

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17

Sevri, Mehmet, Hacer Karacan, and M. Ali Akcayol. "Crime Analysis Based on Association Rules Using Apriori Algorithm." International Journal of Information and Electronics Engineering 7, no. 3 (2017): 99–102. http://dx.doi.org/10.18178/ijiee.2017.7.3.669.

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18

Liu, Gang, Shaobin Huang, Caixia Lu, and Yudan Du. "An improved K-Means Algorithm Based on Association Rules." International Journal of Computer Theory and Engineering 6, no. 2 (2014): 146–49. http://dx.doi.org/10.7763/ijcte.2014.v6.853.

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19

Kongchai, Phaichayon, Nittaya Kerdprasop, and Kittisak Kerdprasop. "The Fuzzy Search for Association Rules with Interestingness Measure." International Journal of Computer Theory and Engineering 6, no. 6 (2014): 490–94. http://dx.doi.org/10.7763/ijcte.2014.v6.915.

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20

Kanimozhi Selvi, C. S., and A. Tamilarasi. "Mining Association rules with Dynamic and Collective Support Thresholds." International Journal of Engineering and Technology 1, no. 3 (2009): 236–40. http://dx.doi.org/10.7763/ijet.2009.v1.44.

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21

Liu, Yong Zhi, and Xue Ping Jia. "Study on Data Classification and Trend of Association Rules of Time Series." Applied Mechanics and Materials 644-650 (September 2014): 2164–68. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2164.

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Association rules has played a significant role in mining classification clear affairs, but the performance is poor for the continuous time series data . Firstly, this paper presents the trend of time series, including the rise, decline and steady trend, and the time series trend method is proposed; Secondly, define the trend of association rules, including the trend of association rules’ support degree, trend of association rule’s confidence; Finally, gives an application example, show the effectiveness of the method in classification and association analysis of time series.

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22

Han, Jianchao, and Mohsen Beheshti. "Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (2006): 287–94. http://dx.doi.org/10.20965/jaciii.2006.p0287.

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Mining association rules is an important task of dara mining and knowledge discovery. Traditional association rules mining is built on transaction databases, which has some limitations. Two of these limitations are 1) each transaction merely contains binary items, meaning that an item either occurs in a transaction or not; 2) only positive association rules are discovered, while negative associations are ignored. Mining fuzzy association rules has been proposed to address the first limitation, while mining algorithms for negative association rules have been developed to resolve the second limi

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23

EL Mahjouby, Mohamed, Mohamed Taj Bennani, Mohamed Lamrini, and Mohamed El Far. "Association rules forecasting for the foreign exchange market." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3443. http://dx.doi.org/10.11591/ijece.v14i3.pp3443-3454.

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Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data

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24

Gandhimathi, D., and N. Anbazhagan. "Extracting of Positive and Negative Association Rules." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 421. http://dx.doi.org/10.23956/ijermt.v6i8.175.

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Association rules analysis is a basic technique to expose how items/patterns are associated to each other. There are two common ways to measure association such as Support and Confidence. Several methods have been proposed in the literature to diminish the number of extracted association rules. Association Rule Mining is one of the greatest current data mining techniques designed to group objects together from huge databases aiming to take out the motivating correlation and relation with massive quantity of data. Association rule mining is used to discover the associated patterns from datasets

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25

El, Mahjouby Mohamed, Bennani Mohamed Taj, Mohamed Lamrini, and Far Mohamed El. "Association rules forecasting for the foreign exchange market." Association rules forecasting for the foreign exchange market 14, no. 3 (2024): 3443–54. https://doi.org/10.11591/ijece.v14i3.pp3443-3454.

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Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). W

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26

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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27

S, Shankar. "A Novel Utility Sentient Approach for Mining Interesting Association Rules." International Journal of Engineering and Technology 1, no. 5 (2009): 454–60. http://dx.doi.org/10.7763/ijet.2009.v1.84.

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28

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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29

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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30

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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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

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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41

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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42

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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43

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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44

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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45

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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46

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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47

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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48

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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49

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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50

kadhiem Jabbar, Emad, and Waheed Abd Al-Kadhiem Salman. "Proposed Parallel Association Rules Algorithm." Engineering and Technology Journal 32, no. 1B (2014): 157–68. http://dx.doi.org/10.30684/etj.32.1b.18.

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