Unsupervised learning algorithms applied to grouping problems
One of the tasks of great interest within process mining is the discovery of business process models, which consists of using an event log as input and producing a business process model by analyzing the data contained in the log and applying a process mining method, task and/or technique. The disco...
- Autores:
-
amelec, viloria
Lizardo Zelaya, Nelson Alberto
Varela, Noel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7654
- Acceso en línea:
- https://hdl.handle.net/11323/7654
https://doi.org/10.1016/j.procs.2020.07.099
https://repositorio.cuc.edu.co/
- Palabra clave:
- Trace grouping
Data mining
Unsupervised learning techniques
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Unsupervised learning algorithms applied to grouping problems |
| title |
Unsupervised learning algorithms applied to grouping problems |
| spellingShingle |
Unsupervised learning algorithms applied to grouping problems Trace grouping Data mining Unsupervised learning techniques |
| title_short |
Unsupervised learning algorithms applied to grouping problems |
| title_full |
Unsupervised learning algorithms applied to grouping problems |
| title_fullStr |
Unsupervised learning algorithms applied to grouping problems |
| title_full_unstemmed |
Unsupervised learning algorithms applied to grouping problems |
| title_sort |
Unsupervised learning algorithms applied to grouping problems |
| dc.creator.fl_str_mv |
amelec, viloria Lizardo Zelaya, Nelson Alberto Varela, Noel |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Lizardo Zelaya, Nelson Alberto Varela, Noel |
| dc.subject.spa.fl_str_mv |
Trace grouping Data mining Unsupervised learning techniques |
| topic |
Trace grouping Data mining Unsupervised learning techniques |
| description |
One of the tasks of great interest within process mining is the discovery of business process models, which consists of using an event log as input and producing a business process model by analyzing the data contained in the log and applying a process mining method, task and/or technique. The discovery allows the identification of the behaviors contained in the cases of the event log in order to detect possible deviations and/or validate that the business process is executed according to the business requirements. This paper presents an approach based on unsupervised learning techniques for the grouping of traces to generate simpler and more understandable models. The algorithms implemented for clustering are K-means, hierarchical agglomerative and density-based spatial clustering of applications with noise (DBSCAN). |
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2020 |
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2020 |
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2021-01-04T21:16:37Z |
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2021-01-04T21:16:37Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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1877-0509 |
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https://hdl.handle.net/11323/7654 |
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https://doi.org/10.1016/j.procs.2020.07.099 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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1877-0509 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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[1] Celebi, M. E., & Aydin, K. (Eds.). (2016). Unsupervised learning algorithms (Vol. 9, p. 103). Springer. [2] Albert, S., Teletin, M., & Czibula, G. (2018). Analysing protein data using unsupervised learning techniques. Int. J. Innovative Comput. Inf. Control, 14(3), 861-880. [3] Suominen, A., Toivanen, H., & Seppänen, M. (2017). Firms' knowledge profiles: Mapping patent data with unsupervised learning. Technological Forecasting and Social Change, 115, 131-142. [4] Banerjee, N., Giannetsos, T., Panaousis, E., & Took, C. C. (2018, July). Unsupervised learning for trustworthy IoT. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. [5] Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Data mining and analytics in the process industry: The role of machine learning. Ieee Access, 5, 20590-20616. [6] Chauhan, R., Kaur, H., & Puri, R. (2017). An Empirical Analysis of Unsupervised Learning Approach on Medical Databases. In Emerging Trends in Electrical, Communications and Information Technologies (pp. 63-70). Springer, Singapore. [7] Srinivas, C., & Rao, C. G. (2019, June). A novel approach for unsupervised learning of software components. In Proceedings of the 5th International Conference on Engineering and MIS (1-6). [8] Fu, W., & Menzies, T. (2017, August). Revisiting unsupervised learning for defect prediction. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (pp. 72-83). [9] Packianather, M. S., Davies, A., Harraden, S., Soman, S., & White, J. (2017). Data mining techniques applied to a manufacturing SME. Procedia CIRP, 62, 123-128. [10] Bokhari, S. M. A., & Khan, S. A. (2016). Applying Supervised and Unsupervised Learning Techniques on Dental Patients’ Records. In Emerging Trends and Advanced Technologies for Computational Intelligence (pp. 83-102). Springer, Cham. [11] Unnisa, M., Ameen, A., Raziuddin, S. (2016). Opinion mining on twitter data using unsupervised learning technique. International Journal of Computer Applications, 148(12), 975-8887. [12] Henkel, J., Lahiri, S. K., Liblit, B., & Reps, T. (2019). Enabling Open-World Specification Mining via Unsupervised Learning. arXiv preprint arXiv:1904.12098. [13] Viloria, A., Guerrero, I. M., Caraballo, H. M., Llinas, N. O., Valero, L., Palma, H. H., … Lezama, O. B. P. (2019). Effect on the demand and stock returns: Cross-sectional of big data and time-series analysis. In Communications in Computer and Information Science (Vol. 1122 CCIS, pp. 211–220). Springer. https://doi.org/10.1007/978-981-15-1301-5_17. [14] Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. (2016, September). Event abstraction for process mining using supervised learning techniques. In Proceedings of SAI Intelligent Systems Conference (pp. 251-269). Springer, Cham. [15] Viloria, A., Angulo, M. G., Kamatkar, S. J., de la Hoz – Hernandez, J., Guiliany, J. G., Bilbao, O. R., & Hernandez-P, H. (2020). Prediction Rules in E-Learning Systems Using Genetic Programming. In Smart Innovation, Systems and Technologies (Vol. 164, pp. 55–63). Springer. https://doi.org/10.1007/978-981-32-9889-7_5 |
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amelec, viloriaLizardo Zelaya, Nelson AlbertoVarela, Noel2021-01-04T21:16:37Z2021-01-04T21:16:37Z20201877-0509https://hdl.handle.net/11323/7654https://doi.org/10.1016/j.procs.2020.07.099Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/One of the tasks of great interest within process mining is the discovery of business process models, which consists of using an event log as input and producing a business process model by analyzing the data contained in the log and applying a process mining method, task and/or technique. The discovery allows the identification of the behaviors contained in the cases of the event log in order to detect possible deviations and/or validate that the business process is executed according to the business requirements. This paper presents an approach based on unsupervised learning techniques for the grouping of traces to generate simpler and more understandable models. The algorithms implemented for clustering are K-means, hierarchical agglomerative and density-based spatial clustering of applications with noise (DBSCAN).amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Lizardo Zelaya, Nelson Alberto-will be generated-orcid-0000-0002-3963-5690-600Varela, Noelapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920317993Trace groupingData miningUnsupervised learning techniquesUnsupervised learning algorithms applied to grouping problemsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Celebi, M. E., & Aydin, K. (Eds.). (2016). Unsupervised learning algorithms (Vol. 9, p. 103). Springer.[2] Albert, S., Teletin, M., & Czibula, G. (2018). Analysing protein data using unsupervised learning techniques. Int. J. Innovative Comput. Inf. Control, 14(3), 861-880.[3] Suominen, A., Toivanen, H., & Seppänen, M. (2017). Firms' knowledge profiles: Mapping patent data with unsupervised learning. Technological Forecasting and Social Change, 115, 131-142.[4] Banerjee, N., Giannetsos, T., Panaousis, E., & Took, C. C. (2018, July). Unsupervised learning for trustworthy IoT. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE.[5] Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Data mining and analytics in the process industry: The role of machine learning. Ieee Access, 5, 20590-20616.[6] Chauhan, R., Kaur, H., & Puri, R. (2017). An Empirical Analysis of Unsupervised Learning Approach on Medical Databases. In Emerging Trends in Electrical, Communications and Information Technologies (pp. 63-70). Springer, Singapore.[7] Srinivas, C., & Rao, C. G. (2019, June). A novel approach for unsupervised learning of software components. In Proceedings of the 5th International Conference on Engineering and MIS (1-6).[8] Fu, W., & Menzies, T. (2017, August). Revisiting unsupervised learning for defect prediction. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (pp. 72-83).[9] Packianather, M. S., Davies, A., Harraden, S., Soman, S., & White, J. (2017). Data mining techniques applied to a manufacturing SME. Procedia CIRP, 62, 123-128.[10] Bokhari, S. M. A., & Khan, S. A. (2016). Applying Supervised and Unsupervised Learning Techniques on Dental Patients’ Records. In Emerging Trends and Advanced Technologies for Computational Intelligence (pp. 83-102). Springer, Cham.[11] Unnisa, M., Ameen, A., Raziuddin, S. (2016). Opinion mining on twitter data using unsupervised learning technique. International Journal of Computer Applications, 148(12), 975-8887.[12] Henkel, J., Lahiri, S. K., Liblit, B., & Reps, T. (2019). Enabling Open-World Specification Mining via Unsupervised Learning. arXiv preprint arXiv:1904.12098.[13] Viloria, A., Guerrero, I. M., Caraballo, H. M., Llinas, N. O., Valero, L., Palma, H. H., … Lezama, O. B. P. (2019). Effect on the demand and stock returns: Cross-sectional of big data and time-series analysis. In Communications in Computer and Information Science (Vol. 1122 CCIS, pp. 211–220). Springer. https://doi.org/10.1007/978-981-15-1301-5_17.[14] Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. (2016, September). Event abstraction for process mining using supervised learning techniques. In Proceedings of SAI Intelligent Systems Conference (pp. 251-269). Springer, Cham.[15] Viloria, A., Angulo, M. G., Kamatkar, S. J., de la Hoz – Hernandez, J., Guiliany, J. G., Bilbao, O. R., & Hernandez-P, H. (2020). Prediction Rules in E-Learning Systems Using Genetic Programming. In Smart Innovation, Systems and Technologies (Vol. 164, pp. 55–63). 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