Association rules implementation for affinity analysis between elements composing multimedia objects
The multimedia objects are a constantly growing resource in the world wide web, consequently it has generated as a necessity the design of methods and tools that allow to obtain new knowledge from the information analyzed. Association rules are a technique of Data Mining, whose purpose is to search...
- Autores:
-
Mendoza Palechor, Fabio
Carrascal Oviedo, Ana
De la Hoz, Emiro
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5262
- Acceso en línea:
- https://hdl.handle.net/11323/5262
https://repositorio.cuc.edu.co/
- Palabra clave:
- Association rules
Multimedia object
Data mining
Data-Set
Correlations
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Association rules implementation for affinity analysis between elements composing multimedia objects |
| title |
Association rules implementation for affinity analysis between elements composing multimedia objects |
| spellingShingle |
Association rules implementation for affinity analysis between elements composing multimedia objects Association rules Multimedia object Data mining Data-Set Correlations |
| title_short |
Association rules implementation for affinity analysis between elements composing multimedia objects |
| title_full |
Association rules implementation for affinity analysis between elements composing multimedia objects |
| title_fullStr |
Association rules implementation for affinity analysis between elements composing multimedia objects |
| title_full_unstemmed |
Association rules implementation for affinity analysis between elements composing multimedia objects |
| title_sort |
Association rules implementation for affinity analysis between elements composing multimedia objects |
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Mendoza Palechor, Fabio Carrascal Oviedo, Ana De la Hoz, Emiro |
| dc.contributor.author.spa.fl_str_mv |
Mendoza Palechor, Fabio Carrascal Oviedo, Ana De la Hoz, Emiro |
| dc.subject.spa.fl_str_mv |
Association rules Multimedia object Data mining Data-Set Correlations |
| topic |
Association rules Multimedia object Data mining Data-Set Correlations |
| description |
The multimedia objects are a constantly growing resource in the world wide web, consequently it has generated as a necessity the design of methods and tools that allow to obtain new knowledge from the information analyzed. Association rules are a technique of Data Mining, whose purpose is to search for correlations between elements of a collection of data (data) as support for decision making from the identification and analysis of these correlations. Using algorithms such as: A priori, Frequent Parent Growth, QFP Algorithm, CBA, CMAR, CPAR, among others. On the other hand, multimedia applications today require the processing of unstructured data provided by multimedia objects, which are made up of text, images, audio and videos. For the storage, processing and management of multimedia objects, solutions have been generated that allow efficient search of data of interest to the end user, considering that the semantics of a multimedia object must be expressed by all the elements that composed of. In this article an analysis of the state of the art in relation to the implementation of the Association Rules in the processing of Multimedia objects is made, in addition the analysis of the consulted literature allows to generate questions about the possibility of generating a method of association rules for the analysis of these objects. |
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2019-03-31 |
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[1] Hu, C., Xu, Z., Liu, Y., Mei, L., Chen, L., & Luo, X. (2014). Semantic link network-based model for organizing multimedia big data. IEEE Transactions on Emerging Topics in Computing, 2(3), 376-387. [2] Tešic, J., Newsam, S., & Manjunath, B. S. (2003). Mining image datasets using perceptual association rules. In Proc. SIAM Sixth Workshop on Mining Scientific and Engineering Datasets in conjunction with SDM. [3] Zheng, Q. F., Wang, W. Q., & Gao, W. (2006, October). Effective and efficient object-based image retrieval using visual phrases. In Proceedings of the 14th ACM international conference on Multimedia (pp. 77-80). ACM. [4] Jiang, T., & Tan, A. H. (2009). Learning image-text associations. IEEE Transactions on Knowledge and Data Engineering, 21(2), 161- 177. [5] Alghamdi, R. A., Taileb, M., & Ameen, M. (2014, April). A new multimodal fusion method based on association rules mining for image retrieval. In Mediterranean Electrotechnical Conference (MELECON), 2014 17th IEEE (pp. 493-499). IEEE. [6] Grosky, W. I. (1997). Managing multimedia information in database systems. Communications of the ACM, 40(12), 72-80. [7] Yang, Y., Zhuang, Y. T., Wu, F., & Pan, Y. H. (2008). Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. Multimedia, IEEE Transactions on, 10(3), 437-446. [8] Zhuang, Y. T., Yang, Y., & Wu, F. (2008). Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. Multimedia, IEEE Transactions on, 10(2), 221- 229. [9] Hunter, J., & Choudhury, S. (2003). Implementing preservation strategies for complex multimedia objects. In Research and Advanced Technology for Digital Libraries (pp. 473-486). Springer Berlin Heidelberg. [10] Swain, M. J., & Ballard, D. H. (1991). Color indexing. International journal of computer vision, 7(1), 11-32. [11] Little, T. D., & Ghafoor, A. (1990). Synchronization and storage models for multimedia objects. Selected Areas in Communications, IEEE Journal on, 8(3), 413- 427. [12] W. Ma and B. S. Manjunath, \A texture thesaurus for browsing large aerial photographs," Journal of the American Society of Information Science, 1998. [13] Malik, H. H., & Kender, J. R. (2006, July). Clustering web images using association rules, interestingness measures, and hypergraph partitions. In Proceedings of the 6th international conference on Web engineering (pp. 48-55). ACM. [14] Chen, C. L., Tseng, F. S., & Liang, T. (2010). An integration of WordNet and fuzzy association rule mining for multi-label document clustering. Data & Knowledge Engineering, 69(11), 1208-1226. [15] Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In ACM SIGMOD Record (Vol. 22, No. 2, pp. 207- 216). ACM. [16] Mustafa, M. D., Nabila, N. F., Evans, D. J., Saman, M. Y., & Mamat, A. (2006). Association rules on significant rare data using second support. International Journal of Computer Mathematics, 83(1), 69-80. [17] Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499). [18] Agrawal, R., & Shafer, J. C. (1996). Parallel mining of association rules. IEEE Transactions on Knowledge & Data Engineering, (6), 962- 969. [19] Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data mining and knowledge discovery, 8(1), 53-87. [20] Hanguang, L., & Yu, N. (2012). Intrusion detection technology research based on apriori algorithm. Physics Procedia, 24, 1615-1620. [21] Xiang, L. I. (2012). Simulation System of Car Crash Test in C-NCAP Analysis Based on an Improved Apriori Algorithm*. Physics Procedia, 25, 2066-2071. [22] Tsuji, K., Takizawa, N., Sato, S., Ikeuchi, U., Ikeuchi, A., Yoshikane, F., & Itsumura, H. (2014). Book Recommendation Based on Library Loan Records and Bibliographic Information. Procedia-Social and Behavioral Sciences, 147, 478-486. [23] Xu, Y., Li, Y., & Shaw, G. (2011). Reliable representations for association rules. Data & Knowledge Engineering, 70(6), 555-575. [24] Domingues., M. (2004).Generalization of association rules (Tesis de Maestria). Escola de Engenharia de São Carlos, Brasil. [25] Zaki, M. J., Parthasarathy, S., Ogihara, M., & Li, W. (1997, August). New Algorithms for Fast Discovery of Association Rules. In KDD (Vol. 97, pp. 283-286). [26] Han, J., Pei, J., & Yin, Y. (2000, May). Mining frequent patterns without candidate generation. In ACM SIGMOD Record (Vol. 29, No. 2, pp. 1-12). ACM. [27] Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995). An efficient algorithm for mining association rules in large databases. [28] Liu., B., Hsu., W., & Ma., Y. (1998, August). Integrating classification and association rule mining. In Proceedings of the fourth international conference on knowledge discovery and data mining. [29] Das, A., Ng, W. K., & Woon, Y. K. (2001, October). Rapid association rule mining. In Proceedings of the tenth international conference on Information and knowledge management (pp. 474-481). ACM. [30] Li, W., Han, J., & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on (pp. 369-376). IEEE. [31] Yin, X., & Han, J. (2003, May). CPAR: Classification based on Predictive Association Rules. In SDM (Vol. 3, pp. 369-376). [32] Thabtah, F., Cowling, P., & Peng, Y. (2004, November). MMAC: A new multi-class, multilabel associative classification approach. In Data Mining, 2004. ICDM'04. Fourth IEEE International Conference on (pp. 217-224). IEEE. [33] Juan, L., & De-ting, M. (2010, October). Research of an association rule mining algorithm based on FP tree. In Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on (Vol. 1, pp. 559-563). IEEE. [34] Narvekar, M., & Syed, S. F. (2015). An Optimized Algorithm for Association Rule Mining Using FP Tree. Procedia Computer Science, 45, 101-110. [35] Bhandari, A., Gupta, A., & Das, D. (2015). Improvised Apriori Algorithm using frequent pattern tree for real time applications in data mining. Procedia Computer Science, 46, 644- 651. [36] Pinho., J. (2010). Métodos de Clasificación basados en asociación aplicados a sistemas de Recomendación (Tesis de Doctorado). Universidad de Salamanca, España. [37] Kotsiantis, S., & Kanellopoulos, D. (2006). Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering, 32(1), 71- 82. [38] Azevedo, P. J., & Jorge, A. M. (2007). Comparing rule measures for predictive association rules. In Machine Learning: ECML 2007 (pp. 510-517). Springer Berlin Heidelberg. |
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Mendoza Palechor, FabioCarrascal Oviedo, AnaDe la Hoz, Emiro2019-09-12T15:42:59Z2019-09-12T15:42:59Z2019-03-311817-31951992-8645https://hdl.handle.net/11323/5262Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The multimedia objects are a constantly growing resource in the world wide web, consequently it has generated as a necessity the design of methods and tools that allow to obtain new knowledge from the information analyzed. Association rules are a technique of Data Mining, whose purpose is to search for correlations between elements of a collection of data (data) as support for decision making from the identification and analysis of these correlations. Using algorithms such as: A priori, Frequent Parent Growth, QFP Algorithm, CBA, CMAR, CPAR, among others. On the other hand, multimedia applications today require the processing of unstructured data provided by multimedia objects, which are made up of text, images, audio and videos. For the storage, processing and management of multimedia objects, solutions have been generated that allow efficient search of data of interest to the end user, considering that the semantics of a multimedia object must be expressed by all the elements that composed of. In this article an analysis of the state of the art in relation to the implementation of the Association Rules in the processing of Multimedia objects is made, in addition the analysis of the consulted literature allows to generate questions about the possibility of generating a method of association rules for the analysis of these objects.Universidad de la Costa, Universidad Pontificia Bolivariana.Mendoza Palechor, Fabio-will be generated-orcid-0000-0002-2755-0841-600Carrascal Oviedo, AnaDe la Hoz, Emiro-will be generated-orcid-0000-0002-4926-7414-600engJournal of Theoretical and Applied Information TechnologyCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Association rulesMultimedia objectData miningData-SetCorrelationsAssociation rules implementation for affinity analysis between elements composing multimedia objectsArtículo 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] Hu, C., Xu, Z., Liu, Y., Mei, L., Chen, L., & Luo, X. (2014). Semantic link network-based model for organizing multimedia big data. IEEE Transactions on Emerging Topics in Computing, 2(3), 376-387. [2] Tešic, J., Newsam, S., & Manjunath, B. S. (2003). Mining image datasets using perceptual association rules. In Proc. SIAM Sixth Workshop on Mining Scientific and Engineering Datasets in conjunction with SDM. [3] Zheng, Q. F., Wang, W. Q., & Gao, W. (2006, October). Effective and efficient object-based image retrieval using visual phrases. In Proceedings of the 14th ACM international conference on Multimedia (pp. 77-80). ACM. [4] Jiang, T., & Tan, A. H. (2009). Learning image-text associations. IEEE Transactions on Knowledge and Data Engineering, 21(2), 161- 177. [5] Alghamdi, R. A., Taileb, M., & Ameen, M. (2014, April). A new multimodal fusion method based on association rules mining for image retrieval. In Mediterranean Electrotechnical Conference (MELECON), 2014 17th IEEE (pp. 493-499). IEEE. [6] Grosky, W. I. (1997). Managing multimedia information in database systems. Communications of the ACM, 40(12), 72-80. [7] Yang, Y., Zhuang, Y. T., Wu, F., & Pan, Y. H. (2008). Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. Multimedia, IEEE Transactions on, 10(3), 437-446. [8] Zhuang, Y. T., Yang, Y., & Wu, F. (2008). Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. Multimedia, IEEE Transactions on, 10(2), 221- 229. [9] Hunter, J., & Choudhury, S. (2003). Implementing preservation strategies for complex multimedia objects. In Research and Advanced Technology for Digital Libraries (pp. 473-486). Springer Berlin Heidelberg. [10] Swain, M. J., & Ballard, D. H. (1991). Color indexing. International journal of computer vision, 7(1), 11-32. [11] Little, T. D., & Ghafoor, A. (1990). Synchronization and storage models for multimedia objects. Selected Areas in Communications, IEEE Journal on, 8(3), 413- 427. [12] W. Ma and B. S. Manjunath, \A texture thesaurus for browsing large aerial photographs," Journal of the American Society of Information Science, 1998. [13] Malik, H. H., & Kender, J. R. (2006, July). Clustering web images using association rules, interestingness measures, and hypergraph partitions. In Proceedings of the 6th international conference on Web engineering (pp. 48-55). ACM. [14] Chen, C. L., Tseng, F. S., & Liang, T. (2010). An integration of WordNet and fuzzy association rule mining for multi-label document clustering. Data & Knowledge Engineering, 69(11), 1208-1226. [15] Agrawal, R., Imieliński, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In ACM SIGMOD Record (Vol. 22, No. 2, pp. 207- 216). ACM. [16] Mustafa, M. D., Nabila, N. F., Evans, D. J., Saman, M. Y., & Mamat, A. (2006). Association rules on significant rare data using second support. International Journal of Computer Mathematics, 83(1), 69-80. [17] Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499). [18] Agrawal, R., & Shafer, J. C. (1996). Parallel mining of association rules. IEEE Transactions on Knowledge & Data Engineering, (6), 962- 969. [19] Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data mining and knowledge discovery, 8(1), 53-87. [20] Hanguang, L., & Yu, N. (2012). Intrusion detection technology research based on apriori algorithm. Physics Procedia, 24, 1615-1620. [21] Xiang, L. I. (2012). Simulation System of Car Crash Test in C-NCAP Analysis Based on an Improved Apriori Algorithm*. Physics Procedia, 25, 2066-2071. [22] Tsuji, K., Takizawa, N., Sato, S., Ikeuchi, U., Ikeuchi, A., Yoshikane, F., & Itsumura, H. (2014). Book Recommendation Based on Library Loan Records and Bibliographic Information. Procedia-Social and Behavioral Sciences, 147, 478-486. [23] Xu, Y., Li, Y., & Shaw, G. (2011). Reliable representations for association rules. Data & Knowledge Engineering, 70(6), 555-575. [24] Domingues., M. (2004).Generalization of association rules (Tesis de Maestria). Escola de Engenharia de São Carlos, Brasil. [25] Zaki, M. J., Parthasarathy, S., Ogihara, M., & Li, W. (1997, August). New Algorithms for Fast Discovery of Association Rules. In KDD (Vol. 97, pp. 283-286). [26] Han, J., Pei, J., & Yin, Y. (2000, May). Mining frequent patterns without candidate generation. In ACM SIGMOD Record (Vol. 29, No. 2, pp. 1-12). ACM. [27] Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995). An efficient algorithm for mining association rules in large databases. [28] Liu., B., Hsu., W., & Ma., Y. (1998, August). Integrating classification and association rule mining. In Proceedings of the fourth international conference on knowledge discovery and data mining. [29] Das, A., Ng, W. K., & Woon, Y. K. (2001, October). Rapid association rule mining. In Proceedings of the tenth international conference on Information and knowledge management (pp. 474-481). ACM. [30] Li, W., Han, J., & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on (pp. 369-376). IEEE. [31] Yin, X., & Han, J. (2003, May). CPAR: Classification based on Predictive Association Rules. In SDM (Vol. 3, pp. 369-376). [32] Thabtah, F., Cowling, P., & Peng, Y. (2004, November). MMAC: A new multi-class, multilabel associative classification approach. In Data Mining, 2004. ICDM'04. Fourth IEEE International Conference on (pp. 217-224). IEEE. [33] Juan, L., & De-ting, M. (2010, October). Research of an association rule mining algorithm based on FP tree. In Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on (Vol. 1, pp. 559-563). IEEE. [34] Narvekar, M., & Syed, S. F. (2015). An Optimized Algorithm for Association Rule Mining Using FP Tree. Procedia Computer Science, 45, 101-110. [35] Bhandari, A., Gupta, A., & Das, D. (2015). Improvised Apriori Algorithm using frequent pattern tree for real time applications in data mining. Procedia Computer Science, 46, 644- 651. [36] Pinho., J. (2010). Métodos de Clasificación basados en asociación aplicados a sistemas de Recomendación (Tesis de Doctorado). Universidad de Salamanca, España. [37] Kotsiantis, S., & Kanellopoulos, D. (2006). Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering, 32(1), 71- 82. [38] Azevedo, P. J., & Jorge, A. M. (2007). Comparing rule measures for predictive association rules. In Machine Learning: ECML 2007 (pp. 510-517). 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