A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application
Besides the need for more advanced predictive methods, there is increasing demand for easily interpretable results. Couples of enhanced association rules (a generalization of association rules/apriori/frequent itemsets) are excellent candidates for this task. They can be interpreted in various ways,...
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| Published in | Journal of intelligent information systems Vol. 62; no. 2; pp. 431 - 458 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.04.2024
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0925-9902 1573-7675 |
| DOI | 10.1007/s10844-023-00820-1 |
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| Abstract | Besides the need for more advanced predictive methods, there is increasing demand for easily interpretable results. Couples of enhanced association rules (a generalization of association rules/apriori/frequent itemsets) are excellent candidates for this task. They can be interpreted in various ways, subgroup discovery being an example. A typical result in rule mining is that there are too low or too many rules in the resulting ruleset. Analysts must usually iterate 5–15 times to get a reasonable number of rules. Inspired by research in a similar area of frequent itemsets to simplify input and parameter-free frequent itemsets, we have proposed a novel algorithm that finds rules based not on parameters like support and confidence but the best rules by a given range of required rule count in output. We propose this algorithm for couples of rules – SD4ft-Miner procedure and benefits from a brand new implementation of methods of mechanizing hypothesis formation in Python called Cleverminer that allows easy implementation of this algorithm. We have verified the algorithm by several applications on eight public data sets. Our original case was a case study, and it was also the reason why we developed the algorithm. However, implementation is in Python, and the algorithm itself can be used on a broader class of methods in any language. The algorithm iterates quickly, in all experiments we needed a maximum of 10 iterations. Possible enhancements to this algorithm are also outlined. |
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| AbstractList | Besides the need for more advanced predictive methods, there is increasing demand for easily interpretable results. Couples of enhanced association rules (a generalization of association rules/apriori/frequent itemsets) are excellent candidates for this task. They can be interpreted in various ways, subgroup discovery being an example. A typical result in rule mining is that there are too low or too many rules in the resulting ruleset. Analysts must usually iterate 5–15 times to get a reasonable number of rules. Inspired by research in a similar area of frequent itemsets to simplify input and parameter-free frequent itemsets, we have proposed a novel algorithm that finds rules based not on parameters like support and confidence but the best rules by a given range of required rule count in output. We propose this algorithm for couples of rules – SD4ft-Miner procedure and benefits from a brand new implementation of methods of mechanizing hypothesis formation in Python called Cleverminer that allows easy implementation of this algorithm. We have verified the algorithm by several applications on eight public data sets. Our original case was a case study, and it was also the reason why we developed the algorithm. However, implementation is in Python, and the algorithm itself can be used on a broader class of methods in any language. The algorithm iterates quickly, in all experiments we needed a maximum of 10 iterations. Possible enhancements to this algorithm are also outlined. |
| Author | Rauch, Jan Máša, Petr |
| Author_xml | – sequence: 1 givenname: Petr orcidid: 0000-0002-1744-034X surname: Máša fullname: Máša, Petr email: masa@petrmasa.com organization: Prague University of Economics and Business – sequence: 2 givenname: Jan orcidid: 0000-0003-1919-7010 surname: Rauch fullname: Rauch, Jan organization: Prague University of Economics and Business |
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| Cites_doi | 10.3233/IDA-160069 10.1109/ACCESS.2023.3295239 10.1371/journal.pone.0179703 10.1007/BF02345483 10.1007/3-540-45372-5_70 10.4018/978-1-60566-010-3.ch258 10.1007/s10618-017-0520-3 10.1201/9781003091448 10.1016/S0020-7373(78)80033-9 10.1007/s10115-010-0356-2 10.1016/S0020-7373(78)80032-7 10.1007/978-3-642-35650-6 10.1016/j.jcss.2009.05.004 10.3233/FI-1999-40403 10.1109/ICDM.2015.87 10.1007/978-94-011-3534-4 10.3390/math9151818 10.1007/978-3-031-25891-6_10 10.3390/electronics11193044 10.1007/s41019-017-0044-2 10.1016/j.knosys.2019.02.019 10.1007/978-3-319-21542-6_4 10.1145/335191.335372 10.1504/IJDMMM.2012.048105 10.1016/S0020-7373(81)80009-0 10.1007/978-3-642-11737-4 10.1007/978-3-319-08326-1_12 10.1007/978-3-642-66943-9 10.1145/170035.170072 10.1002/widm.1144 10.1007/s10115-018-1206-x 10.1007/978-3-642-51883-6_51 10.1007/978-3-030-59491-6_2 10.1016/S0020-7373(77)80012-6 10.1007/978-1-4615-5669-5_1 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | GUHA method Subgroup discovery Enhanced association rules CleverMiner Python |
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| References | Rafea, A.A., Shaalan, K., & Shafik, S. (2004). An interactive system for association rule discovery for life assurance. In H. Chu (Ed.), Proceedings of the 2nd International Conference Computing, Communications and Control Technologies, CCCT 2004, Austin, TX, USA, August 14-17, 2004, (vol. 1, pp. 32–37). The International Institute of Informatics and Systemics (IIIS). Agrawal, R., & Srikant, R.(1994). Fast algorithms for mining association rules in large databases. In 20th International conference on very large data bases, (pp. 487–499). San Francisco: VLDB ’94, Morgan Kaufmann Publishers Inc. http://dl.acm.org/citation.cfm?id=645920.672836 Havránek, T. (1981). The present state of the GUHA software. International Journal of Man-Machine Studies,15(3), 253–264. https://doi.org/10.1016/S0020-7373(81)80009-0. https://www.sciencedirect.com/science/article/pii/S0020737381800090 Šimunek, M., & Rauch, J. (2014). EverMiner prototype using LISp-Miner Control Language. In T. Andreasen, H. Christiansen, J. C. C. Talavera, Z. W. Ras (Eds.) Foundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings. Lecture Notes in Computer Science, (vol. 8502, pp. 113–122). Springer. https://doi.org/10.1007/978-3-319-08326-1_12. Zaki, M.J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). New algorithms for fast discovery of association rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, (pp. 283–286). KDD’97, AAAI Press. Turunen, E., & Dolos, K. (2021). Revealing drivers natural behavior – a GUHA data mining approach. Mathematics,9(15). https://doi.org/10.3390/math9151818. Rauch, J., Šimunek, M., Chudán, D., & Máša, P. (2022). Mechanising hypothesis formation - principles and case studies. CRC Press. https://www.routledge.com/Mechanizing-Hypothesis-Formation-Principles-and-Case-Studies/Rauch-Simunek-Chudan-Masa/p/book/9780367549800 HavránekTChybaMPokornýDProcessing sociological data by the GUHA method - an exampleInternational Journal of Man-Machine Studies19779443944710.1016/S0020-7373(77)80012-6 Hájek, P. (1984). The new version of the GUHA procedure ASSOC. In COMPSTAT 1984, Proceedings in Computational Statistics, (pp. 360–365). https://www.springer.com/gp/book/9783705100077 Powell, L., Gelich, A., & Ras, Z.W. (2020). The construction of action rules to raise artwork prices. In Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020. Lecture Notes in Computer Science, (vol. 12117, pp. 11–20). Springer. https://doi.org/10.1007/978-3-030-59491-6_2. HájekPHoleňaMRauchJThe GUHA method and its meaning for data miningJournal of Computer Systems Science20107613448256578310.1016/j.jcss.2009.05.004 HanJPeiJYinYMining frequent patterns without candidate generationSIGMOD Rec.200029211210.1145/335191.335372 Pawlak, Z. (1991). Rough sets - theoretical aspects of reasoning about data, Theory and decision library: series D, (vol. 9). Kluwer. https://doi.org/10.1007/978-94-011-3534-4. Dua, D., & Graff, C. (2017). UCI machine learning repository. http://archive.ics.uci.edu/ml. Boley, M., Goldsmith, B.R., Ghiringhelli, L.M., & Vreeken, J. (2017). Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery. arXiv:1701.07696. Hahsler, M. (2023). ARULESPY: Exploring Association Rules and Frequent Itemsets in Python HerreraFCarmonaCJGonzálezPdel JesúsMJAn overview on subgroup discovery: foundations and applicationsKnowledge Information Systems201129349552510.1007/s10115-010-0356-2 RauchJEverminer: consideration on knowledge driven permanent data mining processInternational Journal of Data Mining, Modelling and Management20124322424310.1504/IJDMMM.2012.048105 SingaramSJeyakarthicMParameter-free algorithm for mining rare association rulesInternational Journal of Computer Sciences and Engineering201974046 BigML (2023) BigML – Machine learning platform. https://bigml.com/. Accessed: 08 Dec 2023. Li, G., Wang, T., Chen, Q., Shao, P., Xiong, N., & Vasilakos, A. (2022). A survey on particle swarm optimization for association rule mining. Electronics,11(19). https://doi.org/10.3390/electronics11193044. https://www.mdpi.com/2079-9292/11/19/3044. Máša, P., Rauch, J. (2022). GUHA method and Python language. In Proceedings of the 12th Workshop on Uncertainty Processing, (pp. 147–158). MatfyzPress. http://wupes.utia.cas.cz/2022/Proceedings.pdf. Fürnkranz, J., & Kliegr, T. (2015). A brief overview of rule learning. In N. Bassiliades, G. Gottlob, F. Sadri, A. Paschke, & D. Roman (Eds.), Rule Technologies: Foundations, Tools, and Applications - 9th International Symposium, RuleML 2015, Berlin, Germany, August 2-5, 2015, Proceedings. Lecture Notes in Computer Science, (vol. 9202, pp. 54–69). Springer. https://doi.org/10.1007/978-3-319-21542-6_4. SikoraMWróbelLGudysAGuider: A guided separate-and-conquer rule learning in classification, regression, and survival settingsKnowledge Based Systems201917311410.1016/j.knosys.2019.02.019 Egho, E., Gay, D., Boullé, M., Voisine, N., & Clérot, F.: A parameter-free approach for mining robust sequential classification rules. In 2015 IEEE International Conference on Data Mining, (pp. 745–750). https://doi.org/10.1109/ICDM.2015.87. KleeneSCIntroduction to Metamathematics1952Van Nostrand Hahsler, M., Gruen, B., Hornik, K., & Buchta, C. (2015). Mining association rules and frequent itemsets. R package version 1.3-1. http://CRAN.R-project.org/package=arules Ras, Z. W., & Wieczorkowska, A. (2000) Action-rules: How to increase profit of a company. In D. A. Zighed, H. J. Komorowski, J. M. Zytkow (Eds.) Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000, Lyon, France, September 13-16, 2000, Proceedings. Lecture Notes in Computer Science, (vol. 1910, pp. 587–592). Springer. https://doi.org/10.1007/3-540-45372-5_70. Zorrilla, M. E., García-Saiz, D., & Balcázar, J. L. (2011). Towards parameter-free data mining: Mining educational data with yacaree. In Educational Data Mining Hahsler, M., Chelluboina, S., Hornik, K., & Buchta, C. (2011b). The arules R-package ecosystem: Analyzing interesting patterns from large transaction data sets. Journal of Machine Learning Research,12, 2021–2025. http://dl.acm.org/citation.cfm?id=2021064 HájekPHavelIChytilMThe GUHA method of automatic hypotheses determinationComputing19661429330810.1007/BF02345483 AtzmuellerMSubgroup discoveryWIREs Data Mining and Knowledge Discovery201551354910.1002/widm.1144 TurunenEUsing GUHA data mining method in analyzing road traffic accidents occurred in the years 2004–2008 in FinlandData Science Engineering20172322423110.1007/s41019-017-0044-2 Hájek, P., & Havránek, T. (1978). Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer. https://www.springer.com/gp/book/9783540087380. Rauch, J. (2013). Observational Calculi and Association Rules, Studies in Computational Intelligence, (vol. 469). Springer. https://doi.org/10.1007/978-3-642-11737-4 RauchJExpert deduction rules in data mining with association rules: a case studyKnowledge and Information Systems201959116719510.1007/s10115-018-1206-x Máša, P., & Rauch, J. (2022) Enhanced association rules and python. In G. Nicosia, V. Ojha, E. L. Malfa, G. L. Malfa, P. M. Pardalos, G. D. Fatta, G. Giuffrida, & R. Umeton (Eds.) Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Certosa di Pontignano, Italy, September 19-22, 2022, Revised Selected Papers, Part II. Lecture Notes in Computer Science, (vol. 13811, pp. 123–138). Springer. https://doi.org/10.1007/978-3-031-25891-6_10 RauchJSome remarks on computer realizations of GUHA proceduresInternational Journal of Man-Machine Studies1978101232810.1016/S0020-7373(78)80032-7 NieYLuoXYuYA data-driven knowledge discovery framework for smart education management using behavioral characteristicsIEEE Access202311725627257410.1109/ACCESS.2023.3295239 RauchJŠimunekMApriori and GUHA - comparing two approaches to data mining with association rulesIntelligent Data Analysis2017214981101310.3233/IDA-160069 RencZKubátKKouřimJAn application of the GUHA method in medicineInternational Journal of Man-Machine Studies1978101293510.1016/S0020-7373(78)80033-9 Dong, G., & Bailey, J. (2012). Contrast Data Mining: Concepts, Algorithms, and Applications. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Taylor & Francis. https://books.google.cz/books?id=_uxNRbzNdfAC Grzymala-Busse, J.W., & Ziarko, W. (2009). Rough sets and data mining. In: J. Wang (Ed.), Encyclopedia of data warehousing and mining, (2nd ed., vol. 4, pp. 1696–1701). IGI Global. http://www.igi-global.com/Bookstore/Chapter.aspx?TitleId=11046 AqraIHerawanTAbdul GhaniNAkhunzadaAAliABin RazaliRIlahiMRaymond ChooKKA novel association rule mining approach using tid intermediate itemsetPLOS ONE201813113210.1371/journal.pone.0179703 NguyenHSNguyenSHRough sets and association rule generationFundamentals Informaticae1999404383405177494510.3233/FI-1999-40403 Agrawal, R., Imielinski, T., & Swami, A. N. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International conference on management of data, Washington, DC, USA, May 26-28, (pp. 207–216). https://doi.org/10.1145/170035.170072. Dardzinska, A. (2013). Action rules mining. In Studies in Computational Intelligence, (vol. 468). Springer. https://doi.org/10.1007/978-3-642-35650-6. Rauch, J., & Šimunek, M. (2005). An alternative approach to mining association rules. In Foundations of Data Mining and Knowledge Discovery, (pp. 211–231). Springer. https://www.researchgate.net/publication/225673818_An_Alternative_Approach_to_Mining_Association_Rules Hahsler, M., Chelluboina, S., Hornik, K., & Buchta, C. (2011a). The arules r-package ecosystem: Analyzing interesting patterns from large transaction data sets. Journal of Machine Learning Research,12(57), 2021–2025. http://jmlr.org/papers/v12/hahsler11a.html Kliegr, T., 820_CR15 820_CR16 820_CR17 820_CR18 820_CR11 820_CR12 820_CR13 Z Renc (820_CR43) 1978; 10 820_CR14 E Turunen (820_CR48) 2017; 2 820_CR10 820_CR50 T Havránek (820_CR23) 1977; 9 S Singaram (820_CR46) 2019; 7 820_CR9 820_CR8 820_CR7 820_CR6 820_CR49 820_CR5 820_CR2 820_CR45 820_CR1 820_CR47 P Hájek (820_CR20) 2010; 76 F Herrera (820_CR24) 2011; 29 I Aqra (820_CR3) 2018; 13 820_CR37 J Rauch (820_CR41) 2019; 59 SC Kleene (820_CR25) 1952 820_CR38 J Rauch (820_CR40) 2012; 4 820_CR33 820_CR34 820_CR35 820_CR36 820_CR32 J Han (820_CR21) 2000; 29 J Rauch (820_CR42) 2017; 21 M Atzmueller (820_CR4) 2015; 5 820_CR26 820_CR27 P Hájek (820_CR19) 1966; 1 820_CR28 HS Nguyen (820_CR30) 1999; 40 M Sikora (820_CR44) 2019; 173 820_CR29 820_CR22 J Rauch (820_CR39) 1978; 10 Y Nie (820_CR31) 2023; 11 |
| References_xml | – reference: SikoraMWróbelLGudysAGuider: A guided separate-and-conquer rule learning in classification, regression, and survival settingsKnowledge Based Systems201917311410.1016/j.knosys.2019.02.019 – reference: KleeneSCIntroduction to Metamathematics1952Van Nostrand – reference: HerreraFCarmonaCJGonzálezPdel JesúsMJAn overview on subgroup discovery: foundations and applicationsKnowledge Information Systems201129349552510.1007/s10115-010-0356-2 – reference: Egho, E., Gay, D., Boullé, M., Voisine, N., & Clérot, F.: A parameter-free approach for mining robust sequential classification rules. In 2015 IEEE International Conference on Data Mining, (pp. 745–750). https://doi.org/10.1109/ICDM.2015.87. – reference: Agrawal, R., Imielinski, T., & Swami, A. N. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International conference on management of data, Washington, DC, USA, May 26-28, (pp. 207–216). https://doi.org/10.1145/170035.170072. – reference: Hahsler, M., Chelluboina, S., Hornik, K., & Buchta, C. (2011b). The arules R-package ecosystem: Analyzing interesting patterns from large transaction data sets. Journal of Machine Learning Research,12, 2021–2025. http://dl.acm.org/citation.cfm?id=2021064 – reference: Agrawal, R., & Srikant, R.(1994). Fast algorithms for mining association rules in large databases. In 20th International conference on very large data bases, (pp. 487–499). San Francisco: VLDB ’94, Morgan Kaufmann Publishers Inc. http://dl.acm.org/citation.cfm?id=645920.672836 – reference: RauchJŠimunekMApriori and GUHA - comparing two approaches to data mining with association rulesIntelligent Data Analysis2017214981101310.3233/IDA-160069 – reference: Rauch, J. (2013). Observational Calculi and Association Rules, Studies in Computational Intelligence, (vol. 469). Springer. https://doi.org/10.1007/978-3-642-11737-4 – reference: RencZKubátKKouřimJAn application of the GUHA method in medicineInternational Journal of Man-Machine Studies1978101293510.1016/S0020-7373(78)80033-9 – reference: Rauch, J., & Šimunek, M. (2005). An alternative approach to mining association rules. In Foundations of Data Mining and Knowledge Discovery, (pp. 211–231). Springer. https://www.researchgate.net/publication/225673818_An_Alternative_Approach_to_Mining_Association_Rules – reference: Hahsler, M. (2023). ARULESPY: Exploring Association Rules and Frequent Itemsets in Python – reference: Hahsler, M., Gruen, B., Hornik, K., & Buchta, C. (2015). Mining association rules and frequent itemsets. R package version 1.3-1. http://CRAN.R-project.org/package=arules – reference: HanJPeiJYinYMining frequent patterns without candidate generationSIGMOD Rec.200029211210.1145/335191.335372 – reference: Šimunek, M., & Rauch, J. (2014). EverMiner prototype using LISp-Miner Control Language. In T. Andreasen, H. Christiansen, J. C. C. Talavera, Z. W. Ras (Eds.) Foundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings. Lecture Notes in Computer Science, (vol. 8502, pp. 113–122). Springer. https://doi.org/10.1007/978-3-319-08326-1_12. – reference: Grzymala-Busse, J.W., & Ziarko, W. (2009). Rough sets and data mining. In: J. Wang (Ed.), Encyclopedia of data warehousing and mining, (2nd ed., vol. 4, pp. 1696–1701). IGI Global. http://www.igi-global.com/Bookstore/Chapter.aspx?TitleId=11046 – reference: Máša, P., Rauch, J. (2022). GUHA method and Python language. In Proceedings of the 12th Workshop on Uncertainty Processing, (pp. 147–158). MatfyzPress. http://wupes.utia.cas.cz/2022/Proceedings.pdf. – reference: Hahsler, M., Chelluboina, S., Hornik, K., & Buchta, C. (2011a). The arules r-package ecosystem: Analyzing interesting patterns from large transaction data sets. Journal of Machine Learning Research,12(57), 2021–2025. http://jmlr.org/papers/v12/hahsler11a.html – reference: NguyenHSNguyenSHRough sets and association rule generationFundamentals Informaticae1999404383405177494510.3233/FI-1999-40403 – reference: Dua, D., & Graff, C. (2017). UCI machine learning repository. http://archive.ics.uci.edu/ml. – reference: Li, G., Wang, T., Chen, Q., Shao, P., Xiong, N., & Vasilakos, A. (2022). A survey on particle swarm optimization for association rule mining. Electronics,11(19). https://doi.org/10.3390/electronics11193044. https://www.mdpi.com/2079-9292/11/19/3044. – reference: Powell, L., Gelich, A., & Ras, Z.W. (2020). The construction of action rules to raise artwork prices. In Foundations of Intelligent Systems - 25th International Symposium, ISMIS 2020. Lecture Notes in Computer Science, (vol. 12117, pp. 11–20). Springer. https://doi.org/10.1007/978-3-030-59491-6_2. – reference: Hájek, P., & Havránek, T. (1978). Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer. https://www.springer.com/gp/book/9783540087380. – reference: AqraIHerawanTAbdul GhaniNAkhunzadaAAliABin RazaliRIlahiMRaymond ChooKKA novel association rule mining approach using tid intermediate itemsetPLOS ONE201813113210.1371/journal.pone.0179703 – reference: HájekPHoleňaMRauchJThe GUHA method and its meaning for data miningJournal of Computer Systems Science20107613448256578310.1016/j.jcss.2009.05.004 – reference: Havránek, T. (1981). The present state of the GUHA software. International Journal of Man-Machine Studies,15(3), 253–264. https://doi.org/10.1016/S0020-7373(81)80009-0. https://www.sciencedirect.com/science/article/pii/S0020737381800090 – reference: Hájek, P. (1984). The new version of the GUHA procedure ASSOC. In COMPSTAT 1984, Proceedings in Computational Statistics, (pp. 360–365). https://www.springer.com/gp/book/9783705100077 – reference: Dong, G., & Bailey, J. (2012). Contrast Data Mining: Concepts, Algorithms, and Applications. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Taylor & Francis. https://books.google.cz/books?id=_uxNRbzNdfAC – reference: SingaramSJeyakarthicMParameter-free algorithm for mining rare association rulesInternational Journal of Computer Sciences and Engineering201974046 – reference: RauchJEverminer: consideration on knowledge driven permanent data mining processInternational Journal of Data Mining, Modelling and Management20124322424310.1504/IJDMMM.2012.048105 – reference: Kliegr, T., Kuchar, J., Vojír, S., & Zeman, V. (2017) Easyminer - short history of research and current development. In J. Hlavácová (Ed.), Proceedings of the 17th Conference on Information Technologies - Applications and Theory (ITAT 2017), Martinské hole, Slovakia, September 22-26, 2017. CEUR Workshop Proceedings, (vol. 1885, pp. 235–239). CEUR-WS.org. https://ceur-ws.org/Vol-1885/235.pdf – reference: RauchJExpert deduction rules in data mining with association rules: a case studyKnowledge and Information Systems201959116719510.1007/s10115-018-1206-x – reference: TurunenEUsing GUHA data mining method in analyzing road traffic accidents occurred in the years 2004–2008 in FinlandData Science Engineering20172322423110.1007/s41019-017-0044-2 – reference: Boley, M., Goldsmith, B.R., Ghiringhelli, L.M., & Vreeken, J. (2017). Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery. arXiv:1701.07696. – reference: Dardzinska, A. (2013). Action rules mining. In Studies in Computational Intelligence, (vol. 468). Springer. https://doi.org/10.1007/978-3-642-35650-6. – reference: Turunen, E., & Dolos, K. (2021). Revealing drivers natural behavior – a GUHA data mining approach. Mathematics,9(15). https://doi.org/10.3390/math9151818. – reference: AtzmuellerMSubgroup discoveryWIREs Data Mining and Knowledge Discovery201551354910.1002/widm.1144 – reference: Fürnkranz, J., & Kliegr, T. (2015). A brief overview of rule learning. In N. Bassiliades, G. Gottlob, F. Sadri, A. Paschke, & D. Roman (Eds.), Rule Technologies: Foundations, Tools, and Applications - 9th International Symposium, RuleML 2015, Berlin, Germany, August 2-5, 2015, Proceedings. Lecture Notes in Computer Science, (vol. 9202, pp. 54–69). Springer. https://doi.org/10.1007/978-3-319-21542-6_4. – reference: Pawlak, Z. (1991). Rough sets - theoretical aspects of reasoning about data, Theory and decision library: series D, (vol. 9). Kluwer. https://doi.org/10.1007/978-94-011-3534-4. – reference: HájekPHavelIChytilMThe GUHA method of automatic hypotheses determinationComputing19661429330810.1007/BF02345483 – reference: NieYLuoXYuYA data-driven knowledge discovery framework for smart education management using behavioral characteristicsIEEE Access202311725627257410.1109/ACCESS.2023.3295239 – reference: Zaki, M.J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). New algorithms for fast discovery of association rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, (pp. 283–286). KDD’97, AAAI Press. – reference: Ras, Z. W., & Wieczorkowska, A. (2000) Action-rules: How to increase profit of a company. In D. A. Zighed, H. J. Komorowski, J. M. Zytkow (Eds.) Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000, Lyon, France, September 13-16, 2000, Proceedings. Lecture Notes in Computer Science, (vol. 1910, pp. 587–592). Springer. https://doi.org/10.1007/3-540-45372-5_70. – reference: Rauch, J., Šimunek, M., Chudán, D., & Máša, P. (2022). Mechanising hypothesis formation - principles and case studies. CRC Press. https://www.routledge.com/Mechanizing-Hypothesis-Formation-Principles-and-Case-Studies/Rauch-Simunek-Chudan-Masa/p/book/9780367549800# – reference: RauchJSome remarks on computer realizations of GUHA proceduresInternational Journal of Man-Machine Studies1978101232810.1016/S0020-7373(78)80032-7 – reference: BigML (2023) BigML – Machine learning platform. https://bigml.com/. Accessed: 08 Dec 2023. – reference: HavránekTChybaMPokornýDProcessing sociological data by the GUHA method - an exampleInternational Journal of Man-Machine Studies19779443944710.1016/S0020-7373(77)80012-6 – reference: Máša, P., & Rauch, J. (2022) Enhanced association rules and python. In G. Nicosia, V. Ojha, E. L. Malfa, G. L. Malfa, P. M. Pardalos, G. D. Fatta, G. Giuffrida, & R. Umeton (Eds.) Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Certosa di Pontignano, Italy, September 19-22, 2022, Revised Selected Papers, Part II. Lecture Notes in Computer Science, (vol. 13811, pp. 123–138). Springer. https://doi.org/10.1007/978-3-031-25891-6_10 – reference: Rafea, A.A., Shaalan, K., & Shafik, S. (2004). An interactive system for association rule discovery for life assurance. In H. Chu (Ed.), Proceedings of the 2nd International Conference Computing, Communications and Control Technologies, CCCT 2004, Austin, TX, USA, August 14-17, 2004, (vol. 1, pp. 32–37). The International Institute of Informatics and Systemics (IIIS). – reference: Zorrilla, M. E., García-Saiz, D., & Balcázar, J. L. (2011). Towards parameter-free data mining: Mining educational data with yacaree. In Educational Data Mining – volume: 21 start-page: 981 issue: 4 year: 2017 ident: 820_CR42 publication-title: Intelligent Data Analysis doi: 10.3233/IDA-160069 – volume: 11 start-page: 72562 year: 2023 ident: 820_CR31 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3295239 – ident: 820_CR26 – volume: 13 start-page: 1 issue: 1 year: 2018 ident: 820_CR3 publication-title: PLOS ONE doi: 10.1371/journal.pone.0179703 – volume: 1 start-page: 293 issue: 4 year: 1966 ident: 820_CR19 publication-title: Computing doi: 10.1007/BF02345483 – ident: 820_CR35 doi: 10.1007/3-540-45372-5_70 – ident: 820_CR13 doi: 10.4018/978-1-60566-010-3.ch258 – ident: 820_CR7 doi: 10.1007/s10618-017-0520-3 – ident: 820_CR5 – ident: 820_CR1 – ident: 820_CR16 – ident: 820_CR38 doi: 10.1201/9781003091448 – volume: 10 start-page: 29 issue: 1 year: 1978 ident: 820_CR43 publication-title: International Journal of Man-Machine Studies doi: 10.1016/S0020-7373(78)80033-9 – ident: 820_CR9 – volume: 29 start-page: 495 issue: 3 year: 2011 ident: 820_CR24 publication-title: Knowledge Information Systems doi: 10.1007/s10115-010-0356-2 – volume: 10 start-page: 23 issue: 1 year: 1978 ident: 820_CR39 publication-title: International Journal of Man-Machine Studies doi: 10.1016/S0020-7373(78)80032-7 – ident: 820_CR8 doi: 10.1007/978-3-642-35650-6 – volume-title: Introduction to Metamathematics year: 1952 ident: 820_CR25 – volume: 76 start-page: 34 issue: 1 year: 2010 ident: 820_CR20 publication-title: Journal of Computer Systems Science doi: 10.1016/j.jcss.2009.05.004 – volume: 40 start-page: 383 issue: 4 year: 1999 ident: 820_CR30 publication-title: Fundamentals Informaticae doi: 10.3233/FI-1999-40403 – ident: 820_CR11 doi: 10.1109/ICDM.2015.87 – ident: 820_CR32 doi: 10.1007/978-94-011-3534-4 – ident: 820_CR29 – ident: 820_CR47 doi: 10.3390/math9151818 – ident: 820_CR15 – ident: 820_CR6 – ident: 820_CR28 doi: 10.1007/978-3-031-25891-6_10 – ident: 820_CR27 doi: 10.3390/electronics11193044 – volume: 2 start-page: 224 issue: 3 year: 2017 ident: 820_CR48 publication-title: Data Science Engineering doi: 10.1007/s41019-017-0044-2 – volume: 173 start-page: 1 year: 2019 ident: 820_CR44 publication-title: Knowledge Based Systems doi: 10.1016/j.knosys.2019.02.019 – ident: 820_CR12 doi: 10.1007/978-3-319-21542-6_4 – volume: 29 start-page: 1 issue: 2 year: 2000 ident: 820_CR21 publication-title: SIGMOD Rec. doi: 10.1145/335191.335372 – volume: 4 start-page: 224 issue: 3 year: 2012 ident: 820_CR40 publication-title: International Journal of Data Mining, Modelling and Management doi: 10.1504/IJDMMM.2012.048105 – volume: 7 start-page: 40 year: 2019 ident: 820_CR46 publication-title: International Journal of Computer Sciences and Engineering – ident: 820_CR22 doi: 10.1016/S0020-7373(81)80009-0 – ident: 820_CR10 – ident: 820_CR36 doi: 10.1007/978-3-642-11737-4 – ident: 820_CR37 – ident: 820_CR14 – ident: 820_CR45 doi: 10.1007/978-3-319-08326-1_12 – ident: 820_CR18 doi: 10.1007/978-3-642-66943-9 – ident: 820_CR50 – ident: 820_CR2 doi: 10.1145/170035.170072 – volume: 5 start-page: 35 issue: 1 year: 2015 ident: 820_CR4 publication-title: WIREs Data Mining and Knowledge Discovery doi: 10.1002/widm.1144 – volume: 59 start-page: 167 issue: 1 year: 2019 ident: 820_CR41 publication-title: Knowledge and Information Systems doi: 10.1007/s10115-018-1206-x – ident: 820_CR17 doi: 10.1007/978-3-642-51883-6_51 – ident: 820_CR33 doi: 10.1007/978-3-030-59491-6_2 – ident: 820_CR34 – volume: 9 start-page: 439 issue: 4 year: 1977 ident: 820_CR23 publication-title: International Journal of Man-Machine Studies doi: 10.1016/S0020-7373(77)80012-6 – ident: 820_CR49 doi: 10.1007/978-1-4615-5669-5_1 |
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