Multimedia mining: towards the construction of a methodology and a non-structured date analytics tool

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Efrain Alberto Oviedo Carrascal
Ana Isabel Oviedo Carrascal
Gloria Liliana Velez Saldarriaga

Abstract

This research addresses the development of multimedia mining projects by applying analytical techniques to texts, images, audio, and video. In order to develop these projects, a methodology to develop multimedia mining projects (Multimedia Analytical Methodology-MAM) is proposed. Likewise, the construction of a software tool (known as Multimedia Analytical Platform-PAM) which allows the analysis of multimedia mining is introduced. Methodology and platform are evaluated with two study cases on prediction of mammography abnormalities and analysis of medical imaging similarity. Results obtained allowed validating the steps proposed in the MAM methodology and using the PAM platform to extract the characteristics of medical images, to apply data mining techniques, and to satisfactorily evaluate the results obtained.

How to Cite
Oviedo Carrascal, E. A., Oviedo Carrascal, A. I., & Velez Saldarriaga, G. L. (2018). Multimedia mining: towards the construction of a methodology and a non-structured date analytics tool. Revista Ingenierías Universidad De Medellín, 16(31), 125–142. https://doi.org/10.22395/rium.v16n31a6

Article Details

References

[1] X. Wu, X. Zhu, G. Wu y W. Ding, «Data mining with big data», IEEE transactions on knowledge and data engineering, vol. 26, n.º 1, pp. 97-107, 2014.

[2] E. A. Oviedo, A. I. Oviedo y G. L. Vélez, «Minería de datos: aportes y tendencias en el servicio de salud de ciudades inteligentes», Revista Politécnica, vol. 11, n.º 20, pp. 111-120, 2015.

[3] J. Moine, «Metodologías para el descubrimiento de conocimiento en bases de datos: un estudio comparativo. Tesis de Maestría» Universidad Nacional de la Plata, Argentina, 2013.

[4] A. Azevedo y L. Rojão, «KDD, SEMMA and CRISP-DM: a parallel overview», IADS-DM, pp. 182-185, 2008.

[5] O. Maimon y L. Rokach, Data mining and knowledge discovery handbook, New Rork: Springer, 2005.

[6] D. Pyle, Business modeling and data mining, Morgan Kaufmann, 2003.

[7] P. Santana, R. Costaguta y D. Missio, «Aplicación de algoritmos de clasificación de minería de textos para el reconocimiento de habilidades de e-tutores colaborativos», Revista Iberoamericana de Inteligencia Artificial, pp. 57-67, 2014.

[8] M. Tapia, O. Ruiz y C. Chirinos, «Modelo de clasificación de opiniones subjetivas en redes sociales», Ingeniería: Ciencia, Tecnología e Innovación, 2014.

[9] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann y I. H. Witten, «The WEKA Data Mining Software: An Update», SIGKDD Explorations, pp. 10-18, 2009.

[10] M. Hofmann y K. Ralf, RapidMiner: Data Mining Use Cases and Business Analytics Applications, CRC Press, 2013.

[11] L. Torgo, Data mining with R: learning with case studies, Chapman & Hall / CRC., 2010.

[12] B. Devi, K. Rao, S. Setty y M. Rao, «Disaster Prediction System Using IBM SPSS Data Mining Tool», International Journal of Engineering Trends and Technology (IJETT), pp. 3352-3357, 2013.

[13] G. Fernandez, Data mining using SAS applications, CRC Press, 2010.

[14] A. I. Oviedo, J. Perea-Ortega, O. Ortega y E. Sanchis, «Video clustering based on the collaboration of multimedia clusterers,» de CLEI 2012 XXXVIII Conferencia Latinoamericana en Informática, Medellín, 2012.

[15] S. Suganthira, P. Thamilselvan, J. G. R. Sathiaseelan y M. Lakshmiprabha, «A Technical Study on Biomedical image Classification using Mining Algorithms,» de National Conference on Recent Advancements in Software Development (NCRASD-2015), Karaikudi, 2015.

[16] J. Suckling, J. Parker, D. R. Dance, S. Astley, I. Hutt, C. Boggis y J. Savage, «The mammographic image analysis society digital mammogram database,» In Exerpta Medica. International Congress Series, pp. 375-378, 1994.

[17] D. A. Wainwright, I. V. Balyasnikova, A. L. Chang, A. U. Ahmed, K. S. Moon, B. Auffinger y M. S. Lesniak, «IDO Expression in Brain Tumors Increases the Recruitment of Regulatory T Cells and Negatively Impacts Survival,» Clinical cancer research, vol. 18, n.º 22, pp. 6110-6121, 2012.

[18] J. Shiraishi, H. Abe, R. Engelmann y K. Doi, «Effect of high sensitivity in a computerized scheme for detecting extremely subtle solitary pulmonary nodules in chest radiographs: observer performance study», Academic radiology, vol. 10, n.º 11, pp. 1302-1311, 2003.

[19] J. Mena, Data mining your website, Digital Press, 1999.

[20] D. Corrales, A. Ledesma, A. Peña, J. Hoyos, A. Figueroa y J. Corrales, 'A new dataset for coffee rust detection in Colombian crops base on classifiers,' Revista S&T, pp. 9-23, 2014.

[21] J. Riquelme, R. Ruiz y K. Gilbert, 'Minería de datos: Conceptos y tendencias,' vol. 10, nº 29, pp. 11-18, 2006.

[22] D. Torres, 'Diseño y aplicación de una metodología para análisis de noticias policiales utilizando minería de textos,' Universidad de Chile, 2013.
Author Biographies

Efrain Alberto Oviedo Carrascal, Universidad Pontificia Bolivariana

Estudiante de Maestría en TIC en la Universidad Pontificia Bolivariana, Medellín. Correo electrónico: eaoc46@gmail.com

Ana Isabel Oviedo Carrascal, Universidad Pontificia Bolivariana

PhD. Docente Investigadora de la Facultad de Ingeniería en TIC de la Universidad Pontificia Bolivariana, Medellín. Correo electrónico: ana.oviedo@upb.edu.co

Gloria Liliana Velez Saldarriaga, Universidad Pontificia Bolivariana

PhD. Docente Investigadora de la Facultad de Ingeniería en TIC de la Universidad Pontificia Bolivariana, Medellín. Correo electrónico: gloria.velez@upb.edu.co