Relevant bibliographies by topics / AM- Association Rule Mining

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'AM- Association Rule Mining'

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Published: 3 June 2025
Last updated: 23 June 2025

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

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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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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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,  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 Editor Graz University of Technology, Graz, Austria
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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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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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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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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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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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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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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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Dissertations / Theses on the topic "AM- Association Rule Mining"

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Wong, Wai-kit. "Security in association rule mining." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558903.

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Wong, Wai-kit, and 王偉傑. "Security in association rule mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558903.

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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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Zhang, Ya Klein Cerry M. "Association rule mining in cooperative research." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6540.

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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.
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Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.

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HajYasien, Ahmed. "Preserving Privacy in Association Rule Mining." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365286.

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With the development and penetration of data mining within different fields and disciplines, security and privacy concerns have emerged. Data mining technology which reveals patterns in large databases could compromise the information that an individual or an organization regards as private. The aim of privacy-preserving data mining is to find the right balance between maximizing analysis results (that are useful for the common good) and keeping the inferences that disclose private information about organizations or individuals at a minimum. In this thesis we present a new classification for privacy preserving data mining problems, we propose a new heuristic algorithm called the QIBC algorithm that improves the privacy of sensitive knowledge (as itemsets) by blocking more inference channels. We demonstrate the efficiency of the algorithm, we propose two techniques (item count and increasing cardinality) based on item-restriction that hide sensitive itemsets (and we perform experiments to compare the two techniques), we propose an efficient protocol that allows parties to share data in a private way with no restrictions and without loss of accuracy (and we demonstrate the efficiency of the protocol), and we review the literature of software engineering related to the associationrule mining domain and we suggest a list of considerations to achieve better privacy on software.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Information and Communication Technology<br>Faculty of Engineering and Information Technology<br>Full Text
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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.

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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Lin, Weiyang. "Association rule mining for collaborative recommender systems." Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0515100-145926.

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Rantzau, Ralf. "Extended concepts for association rule discovery." [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.

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Ahmed, Shakil. "Strategies for partitioning data in association rule mining." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415661.

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Books on the topic "AM- Association Rule Mining"

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Zhang, Chengqi, and Shichao Zhang, eds. Association Rule Mining. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46027-6.

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1.

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Gkoulalas-Divanis, Aris. Association rule hiding for data mining. Springer, 2010.

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Dass, Rajanish. Classification using association rules. Indian Institute of Management, 2008.

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1978-, Koh Yun Sing, and Rountree Nathan 1974-, eds. Rare association rule mining and knowledge discovery: Technologies for infrequent and critical event detection. Information Science Reference, 2010.

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Kazienko, Przemysław. Associations: Discovery, analysis and applications. Oficyna Wydawnicza Politechniki Wrocławskiej, 2008.

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Zhang, Chengqi, and Shichao Zhang. Association Rule Mining: Models and Algorithms. Springer London, Limited, 2006.

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer, 2012.

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Association Rule Mining: Models and Algorithms (Lecture Notes in Computer Science). Springer, 2002.

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Amolochitis, Emmanouil. Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers, 2022.

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Book chapters on the topic "AM- Association Rule Mining"

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Toivonen, Hannu. "Association Rule." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_38.

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Bramer, Max. "Association Rule Mining I." In Principles of Data Mining. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7307-6_16.

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Bramer, Max. "Association Rule Mining II." In Principles of Data Mining. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7307-6_17.

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Bramer, Max. "Association Rule Mining I." In Principles of Data Mining. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7493-6_16.

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Bramer, Max. "Association Rule Mining II." In Principles of Data Mining. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7493-6_17.

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Bramer, Max. "Association Rule Mining I." In Principles of Data Mining. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4884-5_16.

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Bramer, Max. "Association Rule Mining II." In Principles of Data Mining. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4884-5_17.

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Tan, Ting-Feng, Qing-Guo Wang, Tian-He Phang, Xian Li, Jiangshuai Huang, and Dan Zhang. "Temporal Association Rule Mining." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_24.

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Yu, Philip S., and Yun Chi. "Association Rule Mining on Streams." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_25-2.

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Veloso, Adriano, Bruno Rocha, Márcio de Carvalho, and Wagner Meira. "Real World Association Rule Mining." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45495-0_13.

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Conference papers on the topic "AM- Association Rule Mining"

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Yang, Pu-Tai, Kai-Hao Yang, Ching-Chi Chen, and Shwu-Min Horng. "Subjective Association Rule Mining." In ICMLC 2018: 2018 10th International Conference on Machine Learning and Computing. ACM, 2018. http://dx.doi.org/10.1145/3195106.3195174.

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Hidber, Christian. "Online association rule mining." In the 1999 ACM SIGMOD international conference. ACM Press, 1999. http://dx.doi.org/10.1145/304182.304195.

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Das, Amitabha, Wee-Keong Ng, and Yew-Kwong Woon. "Rapid association rule mining." In the tenth international conference. ACM Press, 2001. http://dx.doi.org/10.1145/502585.502665.

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Davale, Aditya A., and Shailendra W. Shende. "Implementation of coherent rule mining algorithm for association rule mining." In 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE). IEEE, 2015. http://dx.doi.org/10.1109/ablaze.2015.7154920.

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Zhou, Ling, and Stephen Yau. "Association rule and quantitative association rule mining among infrequent items." In the 8th international workshop. ACM Press, 2007. http://dx.doi.org/10.1145/1341920.1341929.

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Roy, Suman, Ripunjoy Bordoloi, Kayboy Jyoti Das, Santosh Kumar, and Monoj Kumar Muchahari. "Association Rule Mining on Crime Pattern Mining." In 2021 International Conference on Computational Performance Evaluation (ComPE). IEEE, 2021. http://dx.doi.org/10.1109/compe53109.2021.9752393.

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Kumar, M. Naresh, and B. Eswara Reddy. "Improved classification association rule mining." In Multi-Agent Systems (IAMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/iama.2009.5228045.

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Lin, Tsau Y. "Sampling in association rule mining." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2004. http://dx.doi.org/10.1117/12.543810.

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Zhou, Xianshan, Liang Wang, and Guangzhu Yu. "Motivation-based association rule mining." In 2010 International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2010. http://dx.doi.org/10.1109/icicip.2010.5564230.

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Liao, Wen-Tsai, and Ming-Syan Chen. "On reconfigurable association rule mining." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6468673.

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