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Journal articles on the topic 'MINING ASSOCIATION RULES'

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Author: Grafiati

Published: 11 September 2023

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1

Pandey, Sachin. "Multilevel Association Rules in Data Mining." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (July 1, 2018): 74–78. http://dx.doi.org/10.29070/15/57517.

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2

Lu, Songfeng, Heping Hu, and Fan Li. "Mining weighted association rules." Intelligent Data Analysis 5, no. 3 (May 1, 2001): 211–25. http://dx.doi.org/10.3233/ida-2001-5303.

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3

Defit, Sarjon. "Intelligent Mining Association Rules." International Journal of Computer Science and Information Technology 4, no. 4 (August 31, 2012): 97–106. http://dx.doi.org/10.5121/ijcsit.2012.4409.

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4

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

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5

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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6

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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7

Ali, Nzar Abdulqader. "Finding minimum confidence threshold to avoid derived rules in association rule minin." Journal of Zankoy Sulaimani - Part A 17, no. 4 (August 30, 2015): 271–78. http://dx.doi.org/10.17656/jzs.10443.

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8

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 association rules in manufacturing, including product design, manufacturing, process, customer relationship management, supply chain management, and product quality improvement. This paper is focused on demonstrating the relevancy of association rules mining to manufacturing industry, rather than discussing the association rules mining domain in general.

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9

Kazienko, Przemysław. "Mining Indirect Association Rules for Web Recommendation." International Journal of Applied Mathematics and Computer Science 19, no. 1 (March 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 indirect associations and complete ones. The former respect single transitive pages, while the latter cover all existing transitive pages. The presented IDARM* Algorithm extracts complete indirect association rules with their important measure—confidence—using pre-calculated direct rules. Both direct and indirect rules are joined into one set of complex association rules, which may be used for the recommendation of web pages. Performed experiments revealed the usefulness of indirect rules for the extension of a typical recommendation list. They also deliver new knowledge not available to direct ones. The relation between ranking lists created on the basis of direct association rules as well as hyperlinks existing on web pages is also examined.

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10

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 (May 20, 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 limitation. In this paper, we combine these two approaches to propose a novel approach for mining both positive and negative fuzzy association rules. The interestingness measure for both positive and negative fuzzy association rule is proposed, the algorithm for mining these rules is described, and an illustrative example is presented to demonstrate how the measure and the algorithm work.

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11

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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12

Liu, Fang, Zhengding Lu, and Songfeng Lu. "Mining association rules using clustering." Intelligent Data Analysis 5, no. 4 (November 8, 2001): 309–26. http://dx.doi.org/10.3233/ida-2001-5403.

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13

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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14

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

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15

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

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16

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 (March 2011): 2129–33. http://dx.doi.org/10.1016/j.asoc.2010.07.011.

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17

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

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18

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

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19

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

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20

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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21

Agrawal, Shivangee, and Nivedita Bairagi. "A Survey for Association Rule Mining in Data Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 245. http://dx.doi.org/10.23956/ijarcsse.v7i8.58.

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Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.

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22

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 transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.

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23

Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.

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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

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24

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 (December 1, 2018): 4568. http://dx.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 non-redundant Association Rules are loss less representation for any datasets.

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25

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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26

Tan, Jun. "Different Types of Association Rules Mining Review." Applied Mechanics and Materials 241-244 (December 2012): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1589.

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In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these 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 technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

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27

U., Deepa, and Nilam K. "Mining Association Rules using R Environment." International Journal of Computer Applications 157, no. 4 (January 17, 2017): 45–50. http://dx.doi.org/10.5120/ijca2017912679.

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28

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

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29

Hong, Tzung-Pei, Chan-Sheng Kuo, and Sheng-Chai Chi. "Mining association rules from quantitative data☆." Intelligent Data Analysis 3, no. 5 (September 1, 1999): 363–76. http://dx.doi.org/10.3233/ida-1999-3504.

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30

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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31

Dong, Liyan, Renbiao Wang, and Yongli Li. "Mining Association Rules Based on Certainty." International Journal of Intelligent Engineering and Systems 5, no. 3 (September 30, 2012): 19–27. http://dx.doi.org/10.22266/ijies2012.9030.03.

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32

Tung, A. K. H., Hongjun Lu, Jiawei Han, and Ling Feng. "Efficient mining of intertransaction association rules." IEEE Transactions on Knowledge and Data Engineering 15, no. 1 (January 2003): 43–56. http://dx.doi.org/10.1109/tkde.2003.1161581.

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33

Tsou, Yao-Tung, Hao Zhen, Xiyu Jiang, Yennun Huang, and Sy-Yen Kuo. "DPARM: Differentially Private Association Rules Mining." IEEE Access 8 (2020): 142131–47. http://dx.doi.org/10.1109/access.2020.3013157.

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34

Lin, Zhang, and Zhang Jianli. "A New Association Rules Mining Algorithm." Journal of Computational and Theoretical Nanoscience 12, no. 9 (September 1, 2015): 2352–55. http://dx.doi.org/10.1166/jctn.2015.4032.

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35

HE, Jun. "Mining of Multi-Relational Association Rules." Journal of Software 18, no. 11 (2007): 2752. http://dx.doi.org/10.1360/jos182752.

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36

Kuok, Chan Man, Ada Fu, and Man Hon Wong. "Mining fuzzy association rules in databases." ACM SIGMOD Record 27, no. 1 (March 1998): 41–46. http://dx.doi.org/10.1145/273244.273257.

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37

Coenen, Frans, Graham Goulbourne, and Paul Leng. "Tree Structures for Mining Association Rules." Data Mining and Knowledge Discovery 8, no. 1 (January 2004): 25–51. http://dx.doi.org/10.1023/b:dami.0000005257.93780.3b.

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38

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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39

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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40

Hong, T. "Mining association rules from quantitative data." Intelligent Data Analysis 3, no. 5 (November 1999): 363–76. http://dx.doi.org/10.1016/s1088-467x(99)00028-1.

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41

Weiß, Christian H. "Statistical mining of interesting association rules." Statistics and Computing 18, no. 2 (December 21, 2007): 185–94. http://dx.doi.org/10.1007/s11222-007-9047-6.

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42

Lopes, A. A., R. Pinho, F. V. Paulovich, and R. Minghim. "Visual text mining using association rules." Computers & Graphics 31, no. 3 (June 2007): 316–26. http://dx.doi.org/10.1016/j.cag.2007.01.023.

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43

Evfimievski, Alexandre, Ramakrishnan Srikant, Rakesh Agrawal, and Johannes Gehrke. "Privacy preserving mining of association rules." Information Systems 29, no. 4 (June 2004): 343–64. http://dx.doi.org/10.1016/j.is.2003.09.001.

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44

Cao, Wen Liang, and Li Ping Chen. "A Distributed Association Rules Mining Algorithm." Advanced Materials Research 971-973 (June 2014): 1459–62. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1459.

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Data mining has attracted a great deal of attention in the information industry in recent years and can be used for applications rangning from business management, production control, and science exploration etc. Most of the existing data mining algorithms are processing in the centralized systems; however, at present large database is usually distributed. Compared with the frequent itemsets lost and high communication traffic in distributed database conventional and improved algorithm FDM, An improved distributed data mining algorithm LTDM based on association roles is proposed. LTDM algorithm introduces the mapping indicated array mechanism to keep the integrity of frequent itemsets and decrease the communication traffic. The experimental results prove the efficiency of the proposed algorithm. The algorithm can be applied to information retrieval and so on in the digital library.

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45

Tebourski, Wafa, and Wahiba Ben Abdesslem Karâa. "Cyclic Association Rules Mining under Constraints." International Journal of Computer Applications 49, no. 20 (July 31, 2012): 30–37. http://dx.doi.org/10.5120/7889-1253.

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46

Shen, Bin, Min Yao, Zhaohui Wu, and Yunjun Gao. "Mining dynamic association rules with comments." Knowledge and Information Systems 23, no. 1 (April 24, 2009): 73–98. http://dx.doi.org/10.1007/s10115-009-0207-1.

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47

Liu, Xiaobing, Kun Zhai, and Witold Pedrycz. "An improved association rules mining method." Expert Systems with Applications 39, no. 1 (January 2012): 1362–74. http://dx.doi.org/10.1016/j.eswa.2011.08.018.

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48

Taniar, David, Wenny Rahayu, Vincent Lee, and Olena Daly. "Exception rules in association rule mining." Applied Mathematics and Computation 205, no. 2 (November 2008): 735–50. http://dx.doi.org/10.1016/j.amc.2008.05.020.

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49

Rodríguez, Andrés, José María Carazo, and Oswaldo Trelles. "Mining association rules from biological databases." Journal of the American Society for Information Science and Technology 56, no. 5 (2005): 493–504. http://dx.doi.org/10.1002/asi.20138.

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50

Palshikar, Girish K., Mandar S. Kale, and Manoj M. Apte. "Association rules mining using heavy itemsets." Data & Knowledge Engineering 61, no. 1 (April 2007): 93–113. http://dx.doi.org/10.1016/j.datak.2006.04.009.

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