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Academic literature on the topic 'MINING ASSOCIATION RULES'

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Published: 11 September 2023

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Journal articles on the topic "MINING ASSOCIATION RULES"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "MINING ASSOCIATION RULES"

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Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.

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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.
Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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Zhou, Zequn. "Maintaining incremental data mining association rules." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62311.pdf.

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Goulbourne, Graham. "Tree algorithms for mining association rules." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250218.

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With the increasing reliability of digital communication, the falling cost of hardware and increased computational power, the gathering and storage of data has become easier than at any other time in history. Commercial and public agencies are able to hold extensive records about all aspects of their operations. Witness the proliferation of point of sale (POS) transaction recording within retailing, digital storage of census data and computerized hospital records. Whilst the gathering of such data has uses in terms of answering specific queries and allowing visulisation of certain trends the volumes of data can hide significant patterns that would be impossible to locate manually. These patterns, once found, could provide an insight into customer behviour, demographic shifts and patient diagnosis hitherto unseen and unexpected. Remaining competitive in a modem business environment, or delivering services in a timely and cost effective manner for public services is a crucial part of modem economics. Analysis of the data held by an organisaton, by a system that "learns" can allow predictions to be made based on historical evidence. Users may guide the process but essentially the software is exploring the data unaided. The research described within this thesis develops current ideas regarding the exploration of large data volumes. Particular areas of research are the reduction of the search space within the dataset and the generation of rules which are deduced from the patterns within the data. These issues are discussed within an experimental framework which extracts information from binary data.
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Koh, Yun Sing, and n/a. "Generating sporadic association rules." University of Otago. Department of Computer Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20070711.115758.

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Association rule mining is an essential part of data mining, which tries to discover associations, relationships, or correlations among sets of items. As it was initially proposed for market basket analysis, most of the previous research focuses on generating frequent patterns. This thesis focuses on finding infrequent patterns, which we call sporadic rules. They represent rare itemsets that are scattered sporadically throughout the database but with high confidence of occurring together. As sporadic rules have low support the minabssup (minimum absolute support) measure was proposed to filter out any rules with low support whose occurrence is indistinguishable from that of coincidence. There are two classes of sporadic rules: perfectly sporadic and imperfectly sporadic rules. Apriori-Inverse was then proposed for perfectly sporadic rule generation. It uses a maximum support threshold and user-defined minimum confidence threshold. This method is designed to find itemsets which consist only of items falling below a maximum support threshold. However imperfectly sporadic rules may contain items with a frequency of occurrence over the maximum support threshold. To look for these rules, variations of Apriori-Inverse, namely Fixed Threshold, Adaptive Threshold, and Hill Climbing, were proposed. However these extensions are heuristic. Thus the MIISR algorithm was proposed to find imperfectly sporadic rules using item constraints, which capture rules with a single-item consequent below the maximum support threshold. A comprehensive evaluation of sporadic rules and current interestingness measures was carried out. Our investigation suggests that current interestingness measures are not suitable for detecting sporadic rules.
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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.
Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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王漣 and Lian Wang. "A study on quantitative association rules." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31223588.

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Wang, Lian. "A study on quantitative association rules /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2118561X.

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Zhu, Hua. "On-line analytical mining of association rules." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ37678.pdf.

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Wu, Jingtong. "Interpretation of association rules with multi-tier granule mining." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71455/1/Jing_Wu_Thesis.pdf.

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This study was a step forward to improve the performance for discovering useful knowledge – especially, association rules in this study – in databases. The thesis proposed an approach to use granules instead of patterns to represent knowledge implicitly contained in relational databases; and multi-tier structure to interpret association rules in terms of granules. Association mappings were proposed for the construction of multi-tier structure. With these tools, association rules can be quickly assessed and meaningless association rules can be justified according to the association mappings. The experimental results indicated that the proposed approach is promising.
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Delpisheh, Elnaz, and University of Lethbridge Faculty of Arts and Science. "Two new approaches to evaluate association rules." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2530.

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Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our second approach dynamically evaluates association rules according to a composite and collective effect of multiple measures. It interactively snapshots the end user’s domain- dependent requirements in evaluating association rules. In essence, our approach uses neural networks along with back-propagation learning to capture the relative importance of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches.
viii, 85 leaves : ill. ; 29 cm
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Books on the topic "MINING ASSOCIATION RULES"

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Kaninis, A. Concurrent Mining of Association Rules. Manchester: UMIST, 1997.

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

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Adamo, Jean-Marc. Data Mining for Association Rules and Sequential Patterns. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4.

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1977-, Zhao Yanchang, Zhang Chengqi 1957-, and Cao Longbing 1969-, eds. Post-mining of association rules: Techniques for effective knowledge extraction. Hershey, PA: Information Science Reference, 2009.

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Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms. New York, NY: Springer New York, 2001.

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Zhang, Chengqi, and Shichao Zhang, eds. Association Rule Mining. Berlin, Heidelberg: 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. Boston, MA: 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. New York: Springer, 2010.

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Kazienko, Przemysław. Associations: Discovery, analysis and applications. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 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. Hershey, PA: Information Science Reference, 2010.

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Book chapters on the topic "MINING ASSOCIATION RULES"

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Atkinson-Abutridy, John. "Association Rules Mining." In Text Analytics, 91–104. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003280996-5.

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Antonie, Luiza, Jundong Li, and Osmar Zaiane. "Negative Association Rules." In Frequent Pattern Mining, 135–45. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_6.

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Höppner, Frank. "Association Rules." In Data Mining and Knowledge Discovery Handbook, 299–319. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_15.

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Ventura, Sebastián, and José María Luna. "Class Association Rules." In Supervised Descriptive Pattern Mining, 99–128. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98140-6_5.

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Zhang, Tao. "Association Rules." In Knowledge Discovery and Data Mining. Current Issues and New Applications, 245–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45571-x_31.

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Triantaphyllou, Evangelos. "Mining of Association Rules." In Data Mining and Knowledge Discovery via Logic-Based Methods, 241–55. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1630-3_12.

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Bembenik, Robert, and Grzegorz Protaziuk. "Mining Spatial Association Rules." In Intelligent Information Processing and Web Mining, 3–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39985-8_1.

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Hamano, Shinichi, and Masako Sato. "Mining Indirect Association Rules." In Advances in Data Mining, 106–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30185-1_12.

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Liu, Bing. "Association Rules and Sequential Patterns." In Web Data Mining, 17–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3_2.

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Dehaspe, Luc, and Hannu Toivonen. "Discovery of Relational Association Rules." In Relational Data Mining, 189–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04599-2_8.

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Conference papers on the topic "MINING ASSOCIATION RULES"

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Li, Jiuyong, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, and Bingyu Sun. "Mining Causal Association Rules." In 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013. http://dx.doi.org/10.1109/icdmw.2013.88.

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Chan, Keith C. C., and Wai-Ho Au. "Mining fuzzy association rules." In the sixth international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/266714.266898.

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Lee, Yue-Shi, and Show-Jane Yen. "Mining Utility Association Rules." In ICCAE 2018: 2018 10th International Conference on Computer and Automation Engineering. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3192975.3192987.

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Selmane, Sid Ali, Rokia Missaoui, Omar Boussaid, and Fadila Bentayeb. "Mining Triadic Association Rules." In Second International Conference on Advanced Information Technologies and Applications. Academy & Industry Research Collaboration Center (AIRCC), 2013. http://dx.doi.org/10.5121/csit.2013.3825.

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Jabas, Ahmad, Rama M. Garimella, and S. Ramachandram. "MANET mining: Mining step association rules." In 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2008. http://dx.doi.org/10.1109/mahss.2008.4660089.

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Jabas, Ahmad, Rama Murtty Garimella, and Sirandas Ramachandram. "MANET Mining: Mining Temporal Association Rules." In 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, 2008. http://dx.doi.org/10.1109/ispa.2008.66.

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Ras, Zbigniew W., Agnieszka Dardzinska, Li-Shiang Tsay, and Hanna Wasyluk. "Association Action Rules." In 2008 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2008. http://dx.doi.org/10.1109/icdmw.2008.66.

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Nahm, Un Yong, and Raymond J. Mooney. "Mining soft-matching association rules." In the eleventh international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/584792.584918.

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Olson, David L., and Yanhong Li. "Mining Fuzzy Weighted Association Rules." In Proceedings of the 40th Annual Hawaii International Conference on System Sciences. IEEE, 2007. http://dx.doi.org/10.1109/hicss.2007.341.

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Goethals, Bart, Juho Muhonen, and Hannu Toivonen. "Mining Non-Derivable Association Rules." In Proceedings of the 2005 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2005. http://dx.doi.org/10.1137/1.9781611972757.22.

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