Relevant bibliographies by topics / Postprocessing of association rules

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

Academic literature on the topic 'Postprocessing of association rules'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Postprocessing of association rules"

1

Szathmáry, László. "Closed Association Rules." Annales Mathematicae et Informaticae 51 (2020): 65–76. http://dx.doi.org/10.33039/ami.2020.07.009.

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Abdullah, Zailani, Tutut Herawan, and Mustafa Mat Deris. "Tracing significant association rules using critical least association rules model." International Journal of Innovative Computing and Applications 5, no. 1 (2013): 3. http://dx.doi.org/10.1504/ijica.2013.052350.

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

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

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

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Zhou, Aoying, Shuigeng Zhou, Wen Jin, and Zengping Tian. "Generalized multidimensional association rules." Journal of Computer Science and Technology 15, no. 4 (2000): 388–92. http://dx.doi.org/10.1007/bf02948876.

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Zhan, Foxiao, Xiaolan Zhu, Lei Zhang, Xuexi Wang, Lu Wang, and Chaoyi Liu. "Summary of Association Rules." IOP Conference Series: Earth and Environmental Science 252 (July 9, 2019): 032219. http://dx.doi.org/10.1088/1755-1315/252/3/032219.

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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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Rauch, Jan. "Logic of Association Rules." Applied Intelligence 22, no. 1 (2005): 9–28. http://dx.doi.org/10.1023/b:apin.0000047380.15356.7a.

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Tzoulis, A., and T. F. Eibert. "Fast computation of electromagnetic near-fields with the multilevel fast multipole method combining near-field and far-field translations." Advances in Radio Science 4 (September 4, 2006): 111–15. http://dx.doi.org/10.5194/ars-4-111-2006.

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Abstract. In Electromagnetic Compatibility (EMC) problems, computation of electromagnetic near-fields in the vicinity of complex radiation and scattering systems is often required. Numerical solution of such problems is achieved using Boundary Integral (BI) based approaches, where the involved Integral Equations (IE's) are solved with the Method of Moments (MoM). The MoM solution process is speeded up by fast IE solvers such as the Multilevel Fast Multipole Method (MLFMM). In the end the desired amplitudes of the expansion of the equivalent current densities on the discrete elements all over t
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Dissertations / Theses on the topic "Postprocessing of association rules"

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Huynh, Xuan-Hiep. "Interestingness Measures for Association Rules in a KDD Process : PostProcessing of Rules with ARQAT Tool." Phd thesis, Université de Nantes, 2006. http://tel.archives-ouvertes.fr/tel-00482649.

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This work takes place in the framework of Knowledge Discovery in Databases (KDD), often called "Data Mining". This domain is both a main research topic and an application ¯eld in companies. KDD aims at discovering previously unknown and useful knowledge in large databases. In the last decade many researches have been published about association rules, which are frequently used in data mining. Association rules, which are implicative tendencies in data, have the advantage to be an unsupervised model. But, in counter part, they often deliver a large number of rules. As a consequence, a postproce
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Huynh, Hiep Xuan. "Interestingness measures for association rules in a KDD process : postprocessing of rules with ARQAT tool." Nantes, 2006. http://www.theses.fr/2006NANT2110.

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Ce travail s'insère dans le cadre de l'extraction de connaissances dans les données (ECD), souvent dénommé "fouille de données". Ce domaine de recherche multidisciplinaire offre également de nombreuses applications en entreprises. L'ECD s'attache à la découverte de connaissances cachées au sein de grandes masses de données. Parmi les modèles d'extraction de connaissances disponibles, celui des règles d'association est fréquemment utilisé. Il offre l'avantage de permettre une découverte non supervisée de tendances implicatives dans les données, mais, en retour, délivre malheureusement de grande
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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
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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.<br>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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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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Matthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.

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Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are ina
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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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Hahsler, Michael, and Radoslaw Karpienko. "Visualizing association rules in hierarchical groups." Springer, 2016. http://dx.doi.org/10.1007/s11573-016-0822-8.

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Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Although visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming, most association rule visualization techniques are still falling short when it comes to large numbers of rules. In this paper we intr
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Pietruszewski, Przemyslaw. "Association rules analysis for objects hierarchy." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3512.

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Association rules are one of the most popular methods of data mining. This technique allows to discover interesting dependences between objects. The thesis concerns on association rules for hierarchy of objects. As a multi–level structure is used DBLP database, which contains bibliographic descriptions of scientific papers conferences and journals in computer science. The main goal of thesis is investigation of interesting patterns of co-authorship with respect to different levels of hierarchy. To reach this goal own extracting method is proposed.<br>p.pietruszewski@op.pl
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Books on the topic "Postprocessing of association rules"

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

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

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Rauch, Jan. Observational Calculi and Association Rules. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-11737-4.

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Rauch, Jan. Observational Calculi and Association Rules. Springer Berlin Heidelberg, 2013.

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Institute, Indian Law. Memorandum of association rules and regulations. s.n., 1991.

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Association of Futures Brokers and Dealers. Rules and handbook. Association of Futures Brokers and Dealers, 1989.

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Association, Nova-Scotia Baptist, and Nova-Scotia Baptist Association. Rules of the Nova Scotia Baptist Association. s.n.], 1993.

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Association, Securities. The Securities Association rules and supplementary information. SecuritiesAssociation, 1987.

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Indonesia, Majelis Ulama. Articles of association, rules of association: National Deliberative Council Result V, 1995. Secretariat of Indonesian Ulama Council, 1997.

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Rules of order. Chicago Review Press, 1992.

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Book chapters on the topic "Postprocessing of association rules"

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Guillaume, Sylvie. "Ordinal Association Rules towards Association Rules." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45228-7_17.

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

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Pei, Jian. "Association Rules." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_26.

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Olson, David L., and Georg Lauhoff. "Association Rules." In Computational Risk Management. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7181-3_5.

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Das, Sibanjan. "Association Rules." In Data Science Using Oracle Data Miner and Oracle R Enterprise. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2614-8_4.

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

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Pei, Jian. "Association Rules." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_26.

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Pei, Jian. "Association Rules." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_26-2.

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Olson, David L. "Association Rules." In Computational Risk Management. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3340-7_5.

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Liiv, Innar. "Association Rules." In Behaviormetrics: Quantitative Approaches to Human Behavior. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2418-6_3.

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Conference papers on the topic "Postprocessing of association rules"

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Karban, Tomáš, Jan Rauch, and Milan Šimůnek. "SDS-rules and association rules." In the 2004 ACM symposium. ACM Press, 2004. http://dx.doi.org/10.1145/967900.968010.

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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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Li, Guichong, and Howard J. Hamilton. "Basic Association Rules." In Proceedings of the 2004 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2004. http://dx.doi.org/10.1137/1.9781611972740.16.

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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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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. ACM Press, 1997. http://dx.doi.org/10.1145/266714.266898.

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Xu, Yue, and Yuefeng Li. "Generating concise association rules." In the sixteenth ACM conference. ACM Press, 2007. http://dx.doi.org/10.1145/1321440.1321549.

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Ruiz, M. Dolores, Juan Gomez-Romero, M. Jose Martin-Bautista, M. Amparo Vila, and Miguel Delgado. "Fuzzy Meta-Association Rules." In 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15). Atlantis Press, 2015. http://dx.doi.org/10.2991/ifsa-eusflat-15.2015.37.

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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. ACM, 2018. http://dx.doi.org/10.1145/3192975.3192987.

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Thummalapenta, Suresh, and Tao Xie. "Mining exception-handling rules as sequence association rules." In 2009 IEEE 31st International Conference on Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icse.2009.5070548.

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