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Journal articles on the topic 'AM- Association Rule Mining'

Author: Grafiati

Published: 3 June 2025

Last updated: 23 June 2025

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1

MRS., KIRAN TIKAR, and KAVITA SURYAWANSHI DR. "A COMPARATIVE STUDY OF ASSOCIATION RULE MINING ALGORITHMS." JournalNX - A Multidisciplinary Peer Reviewed Journal ICACTM (May 3, 2018): 78–80. https://doi.org/10.5281/zenodo.1410059.

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Data mining (DM) techniques is the set of algorithms that helps in extracting interesting patterns and previously unknown facts from larger volume of databases. Todays ever changing customer needs, fluctuation business market and large volume of data generated every second has generated the need of managing and analyzing such a large volume of data. Association Rule mining algorithms helps in identifying correlation between two different items purchased by an individual. Apriori Algorithm and FP-Growth Algorithm are the two algorithms for generating Association Rules. This paper aims at analyze the performance of Apriori and FP-Growth based on speed, efficacy and price and will help in understanding which algorithm is better for a particular situation. https://journalnx.com/journal-article/20150659

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2

Gütl, Christian. "Editorial." JUCS - Journal of Universal Computer Science 30, no. (8) (2024): 1006–7. https://doi.org/10.3897/jucs.134740.

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Dear Readers, It gives me great pleasure to announce the eighth regular issue of 2024. In this issue, 6 papers by 20 authors from 9 countries – Algeria, Brazil, China, Germany, Iraq, Ireland, Pakistan, Turkey, United Kingdom – cover various topical and novel aspects of computer science. As always, I would like to thank all the authors for their sound research and the editorial board and guest reviewers for their extremely valuable review effort and suggestions for improvement. I also want to thank the readers for their interest in our articles, which is reflected in the increasing number of accesses and PDF downloads. These contributions, together with the generous support of the consortium members, sustain the quality of our journal. In a continuous effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. In the eighth regular issue, I am very pleased to introduce the following 6 accepted articles: In a joint research work between Iraq, Algeria and the UK, Rewayda Razaq Abo-Alsabeh, Meryem Cheraitia and Abdellah Salhi discuss their results on a plant propagation algorithm for the bin packing problem. Ildevana Poltronieri, Avelino Francisco Zorzo, Maicon Bernardino and Edson Oliveira Jr from Brazil introduce Usa-DSL, a usability evaluation process for domain-specific languages (DSLs) that aims to assist DSL designers in evaluating their languages in terms of ease and quality of use without requiring deep knowledge of usability evaluation. Carina Heßeling, Sebastian Litzinger and Jörg Keller from Germany report on their research on the archive-based covert channel in sensor streaming data. This is an approach in which the covert sender and receiver first build an archive of values that occur in the stream in a certain time interval, and then encode bits of the secret message via sensor stream values belonging to the class of seen values or not. In another collaborative effort between researchers from Pakistan and Ireland, Anwar Ahmed Khan, Shama Siddiqui and Indrakshi Dey present a novel risk prediction approach, namely Association Rule Mining for Risk Prediction (ARMR), which integrates an IoMT framework with the emerging machine learning technique known as Association Rule Mining (ARM). Furkan Berk Seyrek and Halil Yiğit from Turkey discuss their study, which focuses on the classification of lung images from computed tomography (CT) scans into cancerous and non-cancerous categories by employing prevalent deep learning models, transfer learning, and rigorous evaluation metrics. And last but not least, Yu Zhong, Bo Shen, Tao Wang, Jinglin Zhang and Yun Liu from China address the interaction and fusion of rich textual information for document-level relation extraction that simultaneously considers multiple types of nodes.Enjoy Reading!Best regards, Christian Gütl, Managing EditorGraz University of Technology, Graz, Austria

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3

Gütl, Christian. "Editorial." JUCS - Journal of Universal Computer Science 30, no. 8 (2024): 1006–7. http://dx.doi.org/10.3897/jucs.134740.

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Dear Readers,&nbsp; It gives me great pleasure to announce the eighth regular issue of 2024. In this issue, 6 papers by 20 authors from 9 countries &ndash; Algeria, Brazil, China, Germany, Iraq, Ireland, Pakistan, Turkey, United Kingdom &ndash; cover various topical and novel aspects of computer science. As always, I would like to thank all the authors for their sound research and the editorial board and guest reviewers for their extremely valuable review effort and suggestions for improvement. I also want to thank the readers for their interest in our articles, which is reflected in the increasing number of accesses and PDF downloads. These contributions, together with the generous support of the consortium members, sustain the quality of our journal.&nbsp; In a continuous effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends.&nbsp; In the eighth regular issue, I am very pleased to introduce the following 6 accepted articles: In a joint research work between Iraq, Algeria and the UK, Rewayda Razaq Abo-Alsabeh, Meryem Cheraitia and Abdellah Salhi discuss their results on a plant propagation algorithm for the bin packing problem. Ildevana Poltronieri, Avelino Francisco Zorzo, Maicon Bernardino and Edson Oliveira Jr from Brazil introduce Usa-DSL, a usability evaluation process for domain-specific languages (DSLs) that aims to assist DSL designers in evaluating their languages in terms of ease and quality of use without requiring deep knowledge of usability evaluation. Carina He&szlig;eling, Sebastian Litzinger and J&ouml;rg Keller from Germany report on their research on the archive-based covert channel in sensor streaming data. This is an approach in which the covert sender and receiver first build an archive of values that occur in the stream in a certain time interval, and then encode bits of the secret message via sensor stream values belonging to the class of seen values or not. In another collaborative effort between researchers from Pakistan and Ireland, Anwar Ahmed Khan, Shama Siddiqui and Indrakshi Dey present a novel risk prediction approach, namely Association Rule Mining for Risk Prediction (ARMR), which integrates an IoMT framework with the emerging machine learning technique known as Association Rule Mining (ARM). Furkan Berk Seyrek and Halil Yi&#287;it from Turkey discuss their study, which focuses on the classification of lung images from computed tomography (CT) scans into cancerous and non-cancerous categories by employing prevalent deep learning models, transfer learning, and rigorous evaluation metrics. And last but not least, Yu Zhong, Bo Shen, Tao Wang, Jinglin Zhang and Yun Liu from China address the interaction and fusion of rich textual&nbsp;information for document-level relation extraction that simultaneously considers multiple types of nodes. Enjoy Reading! Best regards,&nbsp; Christian G&uuml;tl, Managing Editor Graz University of Technology, Graz, Austria

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4

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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5

Hidber, Christian. "Online association rule mining." ACM SIGMOD Record 28, no. 2 (1999): 145–56. http://dx.doi.org/10.1145/304181.304195.

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6

PriyankaV., Mahadik, and Kosbatwar Shyam P. "Mining Anomaly using Association Rule." International Journal of Computer Applications 67, no. 24 (2013): 9–12. http://dx.doi.org/10.5120/11734-7338.

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7

Yao, Ran Bo, An Ping Song, Xue Hai Ding, and Ming Bo Li. "Cross Sellingusing Association Rule Mining." Applied Mechanics and Materials 687-691 (November 2014): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1337.

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In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.

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8

Rajavat, Anand, and Pranjal singh solanki. "Modern Association Rule Mining Methods." International Journal of Computational Science and Information Technology 2, no. 4 (2014): 1–9. http://dx.doi.org/10.5121/ijcsity.2014.2401.

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9

LIU, Xu-hui, Shi-huang SHAO, and Guang-zhu YU. "Motivation-based association rule mining." Journal of Computer Applications 29, no. 1 (2009): 189–92. http://dx.doi.org/10.3724/sp.j.1087.2009.00189.

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10

Anand, H. S., and S. S. Vinodchandra. "Association rule mining using treap." International Journal of Machine Learning and Cybernetics 9, no. 4 (2016): 589–97. http://dx.doi.org/10.1007/s13042-016-0546-7.

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11

Kaur, Jagmeet, and Neena Madan. "Association Rule Mining: A Survey." International Journal of Hybrid Information Technology 8, no. 7 (2015): 239–42. http://dx.doi.org/10.14257/ijhit.2015.8.7.22.

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12

YILDIRIM TAŞER, Pelin, Kökten Ulaş BİRANT, and Derya BİRANT. "Multitask-based association rule mining." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 2 (2020): 933–55. http://dx.doi.org/10.3906/elk-1905-88.

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13

Ding, Qin, and William Perrizo. "Support-Less Association Rule Mining Using Tuple Count Cube." Journal of Information & Knowledge Management 06, no. 04 (2007): 271–80. http://dx.doi.org/10.1142/s0219649207001846.

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Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

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14

Wang, Hui. "Strategies for Sensitive Association Rule Hiding." Applied Mechanics and Materials 336-338 (July 2013): 2203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2203.

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Data mining technologies are used widely while the side effects it incurred are concerned so seriously. Privacy preserving data mining is so important for data and knowledge security during data mining applications. Association rule extracted from data mining is one kind of the most popular knowledge. It is challenging to hide sensitive association rules extracted by data mining process and make less affection on non-sensitive rules and the original database. In this work, we focus on specific association rule automatic hiding. Novel strategies are proposed which are based on increasing the support of the left hand and decreasing the support of the right hand. Quality measurements for sensitive association rules hiding are presented.

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15

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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16

Wang, Hui. "Association Rule: From Mining to Hiding." Applied Mechanics and Materials 321-324 (June 2013): 2570–73. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2570.

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Data mining is to discover knowledge which is unknown and hidden in huge database and would be helpful for people understand the data and make decision better. Some knowledge discovered from data mining is considered to be sensitive that the holder of the database will not share because it might cause serious privacy or security problems. Privacy preserving data mining is to hide sensitive knowledge and it is becoming more and more important and attractive. Association rule is one class of the most important knowledge to be mined, so as sensitive association rule hiding. The side-effects of the existing data mining technology are investigated and the representative strategies of association rule hiding are discussed.

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17

Dimitrijevic, Maja, and Zita Bošnjak. "Web Usage Association Rule Mining System." Interdisciplinary Journal of Information, Knowledge, and Management 6 (2011): 137–50. http://dx.doi.org/10.28945/1372.

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18

Yatinkumar Kantilal, Solanki, and Yogesh Kumar Sharma. "UNDERSTANDING ASSOCIATION RULE IN DATA MINING." International Journal of Advanced Research 8, no. 6 (2020): 289–92. http://dx.doi.org/10.21474/ijar01/11097.

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19

Jyothi, N. S. Mrudula, and A. Suraj Kumar. "Privacy Preservation for Association Rule Mining." International Journal of Computer Sciences and Engineering 6, no. 12 (2018): 7–11. http://dx.doi.org/10.26438/ijcse/v6i12.711.

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20

B. Nath, D. K. Bhattacharyya, and A. Ghosh. "Dimensionality Reduction for Association Rule Mining." International Journal of Intelligent Information Processing 2, no. 1 (2011): 9–21. http://dx.doi.org/10.4156/ijiip.vol2.issue1.2.

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21

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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22

Aly Abd Elaty, Amr, Rashed Salem, and Hatem Abdel Kader. "Efficient streaming data association rule mining." النشرة المعلوماتیة فی الحاسبات والمعلومات 1, no. 1 (2019): 35–41. http://dx.doi.org/10.21608/fcihib.2019.107515.

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23

Pathak, Kshitij, Sanjay Silakari, and Narendra S. Chaudhari. "Privacy Preserving Informative Association Rule Mining." International Journal of Applied Information Systems 12, no. 8 (2017): 1–7. http://dx.doi.org/10.5120/ijais2017451717.

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24

Jeudy, Baptiste, and Jean-François Boulicaut. "Optimization of association rule mining queries." Intelligent Data Analysis 6, no. 4 (2002): 341–57. http://dx.doi.org/10.3233/ida-2002-6404.

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25

Nembhard, D. A., K. K. Yip, and C. A. Stifter. "Association Rule Mining in Developmental Psychology." International Journal of Applied Industrial Engineering 1, no. 1 (2012): 23–37. http://dx.doi.org/10.4018/ijaie.2012010103.

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Developmental psychology is the scientific study of progressive psychological changes that occur in human beings as they age. Some of the current methodologies used in this field to study developmental processes include Yule’s Q, state space grids, time series analysis, and lag analysis. The data collected in this field are often time-series-type data. Applying association rule mining in developmental psychology is a new concept that may have a number of potential benefits. In this paper, two sets of infant-mother interaction data sets are examined using association rule mining. Previous analyses of these data used conventional statistical techniques. However, they failed to capture the dynamic interactions between the infant-mother pair as well as other issues relating to the temporal characteristic of the data. Three approaches are proposed in this paper as candidate means of addressing some of the questions that remain from previous studies. The approaches used can be applied to association rule mining to extend its application to data sets in related fields.

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Yan, Xiaowei, Chengqi Zhang, and Shichao Zhang. "CONFIDENCE METRICS FOR ASSOCIATION RULE MINING." Applied Artificial Intelligence 23, no. 8 (2009): 713–37. http://dx.doi.org/10.1080/08839510903208062.

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Surekha, T. lakshmi, P. Rama devi, and J. Malathi. "Profitable Association Rule Mining using Weights." International Journal of Computer Trends and Technology 36, no. 1 (2016): 10–13. http://dx.doi.org/10.14445/22312803/ijctt-v36p102.

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Feng, Feng, Junghoo Cho, Witold Pedrycz, Hamido Fujita, and Tutut Herawan. "Soft set based association rule mining." Knowledge-Based Systems 111 (November 2016): 268–82. http://dx.doi.org/10.1016/j.knosys.2016.08.020.

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29

Zhan, Justin, Stan Matwin, and LiWu Chang. "Privacy-preserving collaborative association rule mining." Journal of Network and Computer Applications 30, no. 3 (2007): 1216–27. http://dx.doi.org/10.1016/j.jnca.2006.04.010.

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Baralis, Elena, Luca Cagliero, Tania Cerquitelli, and Paolo Garza. "Generalized association rule mining with constraints." Information Sciences 194 (July 2012): 68–84. http://dx.doi.org/10.1016/j.ins.2011.05.016.

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31

Margahny, M. H., and A. Shakour. "FAST ALGORITHM FOR MINING ASSOCIATION RULE." JES. Journal of Engineering Sciences 34, no. 1 (2006): 79–87. http://dx.doi.org/10.21608/jesaun.2006.110079.

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32

Jamshaid, Saleha, Zakia Jalil, Malik Sikander Hayat Khiyal, and Muhammad Imran Saeed. "Association Rule Mining in Centralized Databases." Information Technology Journal 6, no. 2 (2007): 174–81. http://dx.doi.org/10.3923/itj.2007.174.181.

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33

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

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34

Nath, B., D. K. Bhattacharyya, and A. Ghosh. "Incremental association rule mining: a survey." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 3 (2013): 157–69. http://dx.doi.org/10.1002/widm.1086.

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35

Shu, Si Hui, and Zi Zhi Lin. "Algorithms of Mining Maximum Frequent Itemsets Based on Compression Matrix." Applied Mechanics and Materials 571-572 (June 2014): 57–62. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.57.

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Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.

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36

Umasankar, P. "Mapper Association Rule Reducer Mining Method (MARRMM) for the Diagnosis of Heart Disease Using Hesitation Rule Set." Asian Journal of Electrical Sciences 8, no. 1 (2019): 15–19. http://dx.doi.org/10.51983/ajes-2019.8.1.2338.

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Association rule is one of the primary tasks in data mining that discovers correlations among items in a transactional database. The majority of vertical and horizontal association rule mining algorithms have been developed to improve the frequent items discovery step which necessitates high demands on training time and memory usage particularly when the input database is very large. In this paper, in the third work, a novel hesitation rule generation method has proposed by blending the Map Reduce concept and Association Rule Mining. In this Mapper Association Rule Reducer Mining method has proposed to generate the hesitation rule set for giving the appropriate medication to the patient who are considered as not getting heart disease.

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37

XU, YUE, and YUEFENG LI. "MINING NON-REDUNDANT ASSOCIATION RULES BASED ON CONCISE BASES." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 04 (2007): 659–75. http://dx.doi.org/10.1142/s0218001407005600.

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Association rule mining has many achievements in the area of knowledge discovery. However, the quality of the extracted association rules has not drawn adequate attention from researchers in data mining community. One big concern with the quality of association rule mining is the size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant, thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a reliable exact association rule basis from which more concise nonredundant rules can be extracted. We prove that the redundancy eliminated using the proposed reliable association rule basis does not reduce the belief to the extracted rules. Moreover, this paper proposes a level wise approach for efficiently extracting closed itemsets and minimal generators — a key issue in closure based association rule mining.

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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 (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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Navale, Geeta S., and Suresh N. Mali. "Survey on Privacy Preserving Association Rule Data Mining." International Journal of Rough Sets and Data Analysis 4, no. 2 (2017): 63–80. http://dx.doi.org/10.4018/ijrsda.2017040105.

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The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

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40

B., Suma, and Shobha G. "Privacy preserving association rule hiding using border based approach." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 1137. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.

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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

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B., Suma, and Shobha G. "Privacy preserving association rule hiding using border based approach." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 1137–45. https://doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.

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Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals, and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy.

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42

Johan, Ragil Andika, Rispani Himilda, and Nadya Auliza. "PENERAPAN METODE ASSOCIATION RULE UNTUK STRATEGI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI." Jurnal Teknik Informatika (J-Tifa) 2, no. 2 (2019): 1–7. http://dx.doi.org/10.52046/j-tifa.v2i2.268.

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Abstrak Persaingan dalam bisnis khususnya dalam bisnis perdagangan semakin banyak. Agar dapat meningkatkan penjualan produk yang dijual, para pelaku harus mempunyai strategi. Salah satu cara yang bisa dilakukan adalah dengan memanfaatkan data transaksi penjualan. Data penjualan tersebut dapat diolah hingga didapatkan informasi yang berguna bagi peningkatan penjualan. Teknologi yang dapat digunakan dalam hal ini adalah data mining. Data mining adalah kegiatan pengolahan data untuk menemukan hubungan dalam suatu data yang berjumlah besar. Suatu metode yang dapat digunakan dalam data mining adalah association rule mining. Association rule mining adalah salah satu metode data mining yang dapat mengidentifikasi hubungan kesamaan antar item. Algoritma yang paling sering dipakai dalam metode ini salah satunya ialah algoritma apriori. Algoritma apriori digunakan untuk mencari kandidat aturan asosiasi. Aturan kombinasi produk berhasil ditemukan dengan penerapan metode assosiation rules menggunakan algoritma apriori dan telah diuji menggunakan tools tanagra. Semua rule yang dihasilkan pada penelitian ini memiliki nilai lift ratio lebih dari 1 sehingga dapat digunakan sebagai acuan dalam membuat strategi penjualan. Kata Kunci : Penjualan, Data Mining, Association Rule, Algoritma Apriori  Abstract Competition in business, especially in the trading business more and more. In order to increase sales of the products, businessman must have a strategy. A things we can do is to use sales transaction data. The sales data can be processed so we will get information of increasing sales. The technology that can be used in this case is data mining. Data mining, often also called knowledge discovery in database (KDD), is a data processing activity to find relationships in a large amount of data. A method that can be used in data mining is association rule mining. Association rule mining is one method of data mining that can identify the similarity relationships between items. One of the most frequently used algorithms in this method is the apriori algorithm. Apriori algorithm is used to find candidate association rules. The product combination rules have been found by applying the association rules method using apriori algorithm and have been tested using tanagra tools. All rules produced in this study have a lift ratio value of more than 1 so it can be used as a reference in making sales strategies. Keywords: Sale, Rule Mining, Association Rule, Apriori Algorithm

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Chen, Yu Ke, and Tai Xiang Zhao. "Association Rule Mining Based on Multidimensional Pattern Relations." Advanced Materials Research 918 (April 2014): 243–45. http://dx.doi.org/10.4028/www.scientific.net/amr.918.243.

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Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide adhoc, query driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis.

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Kou, Zhicong. "Association rule mining using chaotic gravitational search algorithm for discovering relations between manufacturing system capabilities and product features." Concurrent Engineering 27, no. 3 (2019): 213–32. http://dx.doi.org/10.1177/1063293x19832949.

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An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm–based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm; the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm–based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm–based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm–based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm–based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

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Lin, Zi Zhi, Si Hui Shu, and Yun Ding. "Algorithm of Mining Association Rule Based on Matrix." Applied Mechanics and Materials 513-517 (February 2014): 786–91. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.786.

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Association rule mining is one of the most important techniques of data mining. Algorithms based on matrix are efficient due to only scanning the transaction database for one time. In this paper, an algorithm of association rule mining based on the compression matrix is given. It mainly compresses the transaction matrix by integrating various strategies and fleetly finds frequent itemsets. The new algorithm optimizes the known algorithms of mining association rule based on matrix given by some researchers in recent years, which greatly reduces the temporal and spatial complexity, and highly promotes the efficiency of finding frequent itemsets.

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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 problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

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Prahartiwi, Lusa Indah, and Wulan Dari. "Algoritma Apriori untuk Pencarian Frequent itemset dalam Association Rule Mining." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 7, no. 2 (2019): 143–52. http://dx.doi.org/10.33558/piksel.v7i2.1817.

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Abstract  Over decades, retail chains and department stores have been selling their products without using the transactional data generated by their sales as a source of knowledge. Abundant data availability, the need for information (or knowledge) as a support for decision making to create business solutions, and infrastructure support in the field of information technology are the embryos of the birth of data mining technology. Association rule mining is a data mining method used to extract useful patterns between data items. In this research, the Apriori algorithm was applied to find frequent itemset in association rule mining. Data processing using Tanagra tools. The dataset used was the Supermarket dataset consisting of 12 attributes and 108.131 transaction. The experimental results obtained by association rules or rules from the combination of item-sets beer wine spirit-frozen foods and snack foods as a Frequent itemset with a support value of 15.489% and a confidence value of 83.719%. Lift ratio value obtained was 2.47766 which means that there were some benefits from the association rule or rules.   Keywords: Apriori, Association Rule Mining.  Abstrak  Selama beberapa dekade rantai ritel dan department store telah menjual produk mereka tanpa menggunakan data transaksional yang dihasilkan oleh penjualan mereka sebagai sumber pengetahuan. Ketersediaan data yang melimpah, kebutuhan akan informasi (atau pengetahuan) sebagai pendukung pengambilan keputusan untuk membuat solusi bisnis, dan dukungan infrastruktur di bidang teknologi informasi merupakan cikal-bakal dari lahirnya teknologi data mining. Data mining menemukan pola yang menarik dari database seperti association rule, correlations, sequences, classifier dan masih banyak lagi yang mana association rule adalah salah satu masalah yang paling popular. Association rule mining merupakan metode data mining yang digunakan untuk mengekstrasi pola yang bermanfaat di antara data barang. Pada penelitian ini diterapkan algoritma Apriori untuk pencarian frequent itemset dalam association rule mining. Pengolahan data menggunakan tools Tanagra. Dataset yang digunakan adalah dataset Supermarket yang terdiri dari 12 atribut dan 108.131 transaksi. Hasil eksperimen diperoleh aturan asosiasi atau rules dari kombinasi itemsets beer wine spirit-frozen foods dan snack foods sebagai Frequent itemset dengan nilai support sebesar 15,489% dan nilai confidence sebesar 83,719%. Nilai Lift ratio yang diperoleh sebesar 2,47766 yang artinya terdapat manfaat dari aturan asosiasi atau rules tersebut.  Kata kunci: Apriori, Association rule mining

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Duraiswamy, K., and N. Maheswari. "Sensitive Items in Privacy Preserving — Association Rule Mining." Journal of Information & Knowledge Management 07, no. 01 (2008): 31–35. http://dx.doi.org/10.1142/s0219649208001932.

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Privacy-preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data-mining algorithms. For example, through data-mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data-mining problems. There have been two types of privacy concerning data-mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSITEM is proposed. The results of the work have been given.

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Xu, Hong Sheng. "Construction Search Engine Based on Formal Concept Analysis and Association Rule Mining." Advanced Engineering Forum 6-7 (September 2012): 625–30. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.625.

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In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.

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Alasow, Abdirahman, and Marek Perkowski. "Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining." Journal of Quantum Information Science 13, no. 01 (2023): 1–23. http://dx.doi.org/10.4236/jqis.2023.131001.

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