Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm
When working with large data collections to obtain, in a short time, the most relevant information they contain, it is possible to perform pattern extraction by means of Data Mining. Among the most used patterns are Association Rules, which measure the co-occurrence of items in large collections of...
- Autores:
-
Diaz, Jorge
Ovallos-Gazabon, David
Vargas Mercado, Carlos
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10890
- Acceso en línea:
- https://hdl.handle.net/11323/10890
https://repositorio.cuc.edu.co
- Palabra clave:
- Apriori algorithm
Association rules mining (ARM)
Big Data
Recommendation systems (RS)
- Rights
- closedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm |
| title |
Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm |
| spellingShingle |
Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm Apriori algorithm Association rules mining (ARM) Big Data Recommendation systems (RS) |
| title_short |
Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm |
| title_full |
Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm |
| title_fullStr |
Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm |
| title_full_unstemmed |
Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm |
| title_sort |
Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm |
| dc.creator.fl_str_mv |
Diaz, Jorge Ovallos-Gazabon, David Vargas Mercado, Carlos |
| dc.contributor.author.none.fl_str_mv |
Diaz, Jorge Ovallos-Gazabon, David Vargas Mercado, Carlos |
| dc.subject.proposal.eng.fl_str_mv |
Apriori algorithm Association rules mining (ARM) Big Data Recommendation systems (RS) |
| topic |
Apriori algorithm Association rules mining (ARM) Big Data Recommendation systems (RS) |
| description |
When working with large data collections to obtain, in a short time, the most relevant information they contain, it is possible to perform pattern extraction by means of Data Mining. Among the most used patterns are Association Rules, which measure the co-occurrence of items in large collections of transactions. After many trials, one type of transaction has been found that can be treated more efficiently than the one that has been used up to now. In this study, a new method was applied to this type of transactions, which allowed to obtain in the first tests much faster execution times and more information than the one obtained with the classic Association Rules Mining Algorithms. This will allow to improve the response times of a web recommendation system to provide answers to the users in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. |
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2021 |
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2024-03-19T15:44:53Z |
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Diaz, J., Ovallos-Gazabon, D., & Mercado, C. V. (2021). Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm. Lecture Notes in Networks and Systems, 180, 241–248. https://doi.org/10.1007/978-981-33-4788-5_20 |
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Diaz, J., Ovallos-Gazabon, D., & Mercado, C. V. (2021). Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm. Lecture Notes in Networks and Systems, 180, 241–248. https://doi.org/10.1007/978-981-33-4788-5_20 2367-3370 10.1007/978-981-33-4788-5_20 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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Bennet, J., Lanning, S. The Netflix prizes (2007) Proceedings of KDD Cup and Workshop, pp. 3-6. Cited 1169 times. Heaton, J. Comparing dataset characteristics that favor the Apriori, Eclat or FP-Growth frequent itemset mining algorithms (2016) Conference Proceedings - IEEE SOUTHEASTCON, 2016-July, art. no. 7506659. Cited 51 times. ISBN: 978-150902246-5 doi: 10.1109/SECON.2016.7506659 Https://goo.gl/MY3oVv Kim, J., Ale, J. (2004) Descubrimiento Incremental De Las Reglas De asociación Temporales. Cited 2 times. Lee, J., Lee, D., Lee, Y.-C., Hwang, W.-S., Kim, S.-W. Improving the accuracy of top-N recommendation using a preference model (Open Access) (2016) Information Sciences, 348, pp. 290-304. Cited 78 times. http://www.journals.elsevier.com/information-sciences/ doi: 10.1016/j.ins.2016.02.005 Linyuan, L., Medo, M., Chi Ho, Y., Yi-Cheng, Z., Zi-Ke, Z., Tao, Z. (2012) Recommender Systems, pp. 1-49. Cited 4 times. Elsevier B.V López Puga, J.G. Las redes bayesianas como herramientas de modelado en psicología (2007) Anales De Psicología. Redalyc, pp. 307-3016. Cited 20 times. Matich, D.J. (2001) Redes Neuronales: Conceptos básicos Y Aplicaciones. Cátedra De Informática Aplicada a La Ingeniería De Procesos-Orientación. Cited 54 times. Rosario Moine, J., Gordillo, S., Haedo, A. (2011) Análisis Comparativo De metodologías Para La gestión De Proyectos De Minería De Datos. Cited 2 times. Moreno, A., Redondo, T. Text analytics: The convergence of Big Data and artificial intelligence (2016) Int. J Interact Multimed Artif Intell, p. 2016. Cited 89 times. Retail—origin and Meaning of Retail by Online Etymology Dictionary. Cited 2 times. https://goo.gl/zzwvu2 Silva, J., Cubillos, J., Villa, J.V., Romero, L., Solano, D., Fernández, C. Preservation of confidential information privacy and association rule hiding for data mining: A bibliometric review (2019) Procedia Computer Science, 151, pp. 1219-1224. Cited 13 times. http://www.sciencedirect.com/science/journal/18770509 doi: 10.1016/j.procs.2019.04.175 Silva, J., Solano, D., Fernandez, C., Romero, L., Villa, J.V. Privacy preserving, protection of personal data, and big data: A review of the Colombia case. (2019) Procedia Computer Science, 151, pp. 1213-1218. Cited 8 times. http://www.sciencedirect.com/science/journal/18770509 doi: 10.1016/j.procs.2019.04.174 Silva, J., Varela, N., Pineda Lezama, O.B., Hernández-P, H., Martínez Ventura, J., de la Hoz, B., Pérez Coronel, L. Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles (Open Access) (2019) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11555 LNCS, pp. 200-209. Cited 6 times. https://www.springer.com/series/558 ISBN: 978-303022807-1 doi: 10.1007/978-3-030-22808-8_21 Sosa-Cabrera, G., García-Torres, M., Gómez, S., Schaerer, C., Divina, F. Understanding a version of multivariate symmetric uncertainty to assist in feature selection (2016) Conference of Computational Interdisciplinary Science. Cited 2 times. Taneja, A., Arora, A. Cross domain recommendation using multidimensional tensor factorization (Open Access) (2018) Expert Systems with Applications, 92, pp. 304-316. Cited 58 times. doi: 10.1016/j.eswa.2017.09.042. Tovar, L., Montoya, J., Martelo, R. (2018) Sistema ecléctico De Filtrado De información Basado En Inteligencia Computacional Para recomendación De Atractivos turísticos Del Caribe Colombiano. Cited 2 times. Recuperado el 11 de 07 de 2018, de http://revistas.sena.edu.co/index.php/LOG/article/dow nload/1521/1692 Viloria, A., Robayo, P.V. Virtual network level of application composed IP networks connected with systems - (NETS Peer-to- Peer) (2016) Indian Journal of Science and Technology, 9 (46), art. no. 107376. Cited 20 times. http://www.indjst.org/index.php/indjst/article/viewFile/107376/76088 doi: 10.17485/ijst/2016/v9i46/107376 Witten, I., Frank, E. 2000) Data Mining: Practical Machine Learning Tools with Java Implementations. Cited 16156 times. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)Copyright 2023 Elsevier B.V., All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbDiaz, JorgeOvallos-Gazabon, DavidVargas Mercado, Carlos2024-03-19T15:44:53Z2024-03-19T15:44:53Z2021Diaz, J., Ovallos-Gazabon, D., & Mercado, C. V. (2021). Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm. Lecture Notes in Networks and Systems, 180, 241–248. https://doi.org/10.1007/978-981-33-4788-5_202367-3370https://hdl.handle.net/11323/1089010.1007/978-981-33-4788-5_20Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.coWhen working with large data collections to obtain, in a short time, the most relevant information they contain, it is possible to perform pattern extraction by means of Data Mining. Among the most used patterns are Association Rules, which measure the co-occurrence of items in large collections of transactions. After many trials, one type of transaction has been found that can be treated more efficiently than the one that has been used up to now. In this study, a new method was applied to this type of transactions, which allowed to obtain in the first tests much faster execution times and more information than the one obtained with the classic Association Rules Mining Algorithms. This will allow to improve the response times of a web recommendation system to provide answers to the users in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.1 páginaapplication/pdfengSpringer International Publishing AGSwitzerlandhttps://www.scopus.com/record/display.uri?eid=2-s2.0-85147005030&doi=10.1007%2f978-981-33-4788-5_20&origin=inward&txGid=3d102aeb08e19e0112c00fbbaa3008eeAssociation Rules Extraction from Date’s Product Dataset Using the Apriori AlgorithmArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Lecture Notes in Networks and SystemsBennet, J., Lanning, S. The Netflix prizes (2007) Proceedings of KDD Cup and Workshop, pp. 3-6. Cited 1169 times.Heaton, J. Comparing dataset characteristics that favor the Apriori, Eclat or FP-Growth frequent itemset mining algorithms (2016) Conference Proceedings - IEEE SOUTHEASTCON, 2016-July, art. no. 7506659. Cited 51 times. ISBN: 978-150902246-5 doi: 10.1109/SECON.2016.7506659Https://goo.gl/MY3oVvKim, J., Ale, J. (2004) Descubrimiento Incremental De Las Reglas De asociación Temporales. Cited 2 times.Lee, J., Lee, D., Lee, Y.-C., Hwang, W.-S., Kim, S.-W. Improving the accuracy of top-N recommendation using a preference model (Open Access) (2016) Information Sciences, 348, pp. 290-304. Cited 78 times. http://www.journals.elsevier.com/information-sciences/ doi: 10.1016/j.ins.2016.02.005Linyuan, L., Medo, M., Chi Ho, Y., Yi-Cheng, Z., Zi-Ke, Z., Tao, Z. (2012) Recommender Systems, pp. 1-49. Cited 4 times. Elsevier B.VLópez Puga, J.G. Las redes bayesianas como herramientas de modelado en psicología (2007) Anales De Psicología. Redalyc, pp. 307-3016. Cited 20 times.Matich, D.J. (2001) Redes Neuronales: Conceptos básicos Y Aplicaciones. Cátedra De Informática Aplicada a La Ingeniería De Procesos-Orientación. Cited 54 times. RosarioMoine, J., Gordillo, S., Haedo, A. (2011) Análisis Comparativo De metodologías Para La gestión De Proyectos De Minería De Datos. Cited 2 times.Moreno, A., Redondo, T. Text analytics: The convergence of Big Data and artificial intelligence (2016) Int. J Interact Multimed Artif Intell, p. 2016. Cited 89 times.Retail—origin and Meaning of Retail by Online Etymology Dictionary. Cited 2 times. https://goo.gl/zzwvu2Silva, J., Cubillos, J., Villa, J.V., Romero, L., Solano, D., Fernández, C. Preservation of confidential information privacy and association rule hiding for data mining: A bibliometric review (2019) Procedia Computer Science, 151, pp. 1219-1224. Cited 13 times. http://www.sciencedirect.com/science/journal/18770509 doi: 10.1016/j.procs.2019.04.175Silva, J., Solano, D., Fernandez, C., Romero, L., Villa, J.V. Privacy preserving, protection of personal data, and big data: A review of the Colombia case. (2019) Procedia Computer Science, 151, pp. 1213-1218. Cited 8 times. http://www.sciencedirect.com/science/journal/18770509 doi: 10.1016/j.procs.2019.04.174Silva, J., Varela, N., Pineda Lezama, O.B., Hernández-P, H., Martínez Ventura, J., de la Hoz, B., Pérez Coronel, L. Multi-dimension Tensor Factorization Collaborative Filtering Recommendation for Academic Profiles (Open Access) (2019) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11555 LNCS, pp. 200-209. Cited 6 times. https://www.springer.com/series/558 ISBN: 978-303022807-1 doi: 10.1007/978-3-030-22808-8_21Sosa-Cabrera, G., García-Torres, M., Gómez, S., Schaerer, C., Divina, F. Understanding a version of multivariate symmetric uncertainty to assist in feature selection (2016) Conference of Computational Interdisciplinary Science. Cited 2 times.Taneja, A., Arora, A. Cross domain recommendation using multidimensional tensor factorization (Open Access) (2018) Expert Systems with Applications, 92, pp. 304-316. Cited 58 times. doi: 10.1016/j.eswa.2017.09.042.Tovar, L., Montoya, J., Martelo, R. (2018) Sistema ecléctico De Filtrado De información Basado En Inteligencia Computacional Para recomendación De Atractivos turísticos Del Caribe Colombiano. Cited 2 times. Recuperado el 11 de 07 de 2018, de http://revistas.sena.edu.co/index.php/LOG/article/dow nload/1521/1692Viloria, A., Robayo, P.V. Virtual network level of application composed IP networks connected with systems - (NETS Peer-to- Peer) (2016) Indian Journal of Science and Technology, 9 (46), art. no. 107376. Cited 20 times. http://www.indjst.org/index.php/indjst/article/viewFile/107376/76088 doi: 10.17485/ijst/2016/v9i46/107376Witten, I., Frank, E. 2000) Data Mining: Practical Machine Learning Tools with Java Implementations. Cited 16156 times.180Apriori algorithmAssociation rules mining (ARM)Big DataRecommendation systems (RS)PublicationORIGINALAssociation Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm.pdfAssociation Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm.pdfArtículoapplication/pdf133380https://repositorio.cuc.edu.co/bitstreams/25c60a9d-dce3-4f3a-826e-b1bbeca09449/downloadb2cf7299a42fe1abe6c7a4388b277c41MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/01d1fc14-72d3-44d7-8b35-dc6b9951db9f/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTAssociation Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm.pdf.txtAssociation Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm.pdf.txtExtracted texttext/plain5241https://repositorio.cuc.edu.co/bitstreams/489cc02d-b31c-4330-940c-fcfa53e1dd79/download3276cc1784bf1ecead7e9a6360a58cefMD53THUMBNAILAssociation Rules 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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