Análisis de Información

Objetivos

  • Conocer y explorar los métodos más importante para extracción, procesamiento, análisis y descubrimiento de información en bases de datos (texto, imagenes, audio o video)

  • Introducir a los estudiantes en algunas de las lineas de investigación que mas se ha desarrollado en los ultimos 20 años desde las ciencias de la computación con aplicaciones en multiples areas (economia, mercadotecnia, medicina, fisica, astronomia, genetica, entre otras.)

Metodología

    • Se realizarán seminarios semanales sobre cada uno de los temas con ejemplos practicos de aplicación.

    • El componente practico se evaluará en dos formas, la primera con talleres practicos de cada tema visto en clase, la segunda por medio de un proyecto de semestre demostrando la capacidad de aplicar los métodos vistos en clase aplicados a un problema real.

    • Una parte importante del curso es la formación investigativa por ser temas de actualidad, por lo cual el ejercicio de lecturas de articulos científicos se hara por medio de foros de discusión de articulos asignados a los estudiantes.

Contenido

Programación

Google Spreadsheet

Evaluación

    • Ejercicios Practicos 65%

      • Practica I 13%

    • Practica II 13%

    • Practica III 13%

    • Practica IV 13%

    • Practica V 13%

    • Lecturas 10%

    • Proyecto final 25%

Notas

Bibliografía

    • Tan, P., Steinbach, M., and Kumar, V. 2005 Introduction to Data Mining, (First Edition). Addison-Wesley Longman Publishing Co., Inc. [ACM] -- LIBRO GUIA

    • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. [URL]

    • Alpaydin, E. 2004 Introduction to Machine Learning (Adaptive Computation and Machine Learning). The MIT Press. [URL][GoogleBook]

Recursos

Lecturas de Interes

  • Clifford Stoll. El Huevo del Cuco. [PDF]

Papers

  • Hampapur, A. 1995 Designing Video Data Management Systems. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-27641., University of Michigan. [Wilmer Ferran y Monica Maria]

    • Ghias, A., Logan, J., Chamberlin, D., and Smith, B. C. 1995. Query by humming: musical information retrieval in an audio database. In Proceedings of the Third ACM international Conference on Multimedia (San Francisco, California, United States, November 05 - 09, 1995). MULTIMEDIA '95. ACM, New York, NY, 231-236. DOI= http://doi.acm.org/10.1145/217279.215273 [Neyder Angarita Osorio]

  • Gupta, A. and Jain, R. 1997. Visual information retrieval. Commun. ACM 40, 5 (May. 1997), 70-79. DOI= http://doi.acm.org/10.1145/253769.253798

    • Goebel, M. and Gruenwald, L. 1999. A survey of data mining and knowledge discovery software tools. SIGKDD Explor. Newsl. 1, 1 (Jun. 1999), 20-33. DOI= http://doi.acm.org/10.1145/846170.846172 [Juan David Aldana, Sori Andrea Fernandez]

  • Zaïane, O. R., Han, J., Li, Z., and Hou, J. 1998. Mining multimedia data. In Proceedings of the 1998 Conference of the Centre For Advanced Studies on Collaborative Research (Toronto, Ontario, Canada, November 30 - December 03, 1998). S. A. MacKay and J. H. Johnson, Eds. IBM Centre for Advanced Studies Conference. IBM Press, 24.

  • Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., and Euler, T. 2006. YALE: rapid prototyping for complex data mining tasks. In Proceedings of the 12th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining(Philadelphia, PA, USA, August 20 - 23, 2006). KDD '06. ACM, New York, NY, 935-940. DOI= http://doi.acm.org/10.1145/1150402.1150531

  • Kosala, R. and Blockeel, H. 2000. Web mining research: a survey. SIGKDD Explor. Newsl. 2, 1 (Jun. 2000), 1-15. DOI= http://doi.acm.org/10.1145/360402.360406

  • Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. 1996. The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39, 11 (Nov. 1996), 27-34. DOI= http://doi.acm.org/10.1145/240455.240464 [Leonardo Reina, Richard Morales]

  • N. Zhong, J. Liu, Y. Y. Yao, S. Ohsuga, "Web Intelligence (WI)," Computer Software and Applications Conference, Annual International, p. 469, The Twenty-Fourth Annual International Computer Software and Applications Conference, 2000

  • Tong, S. and Chang, E. 2001. Support vector machine active learning for image retrieval. In Proceedings of the Ninth ACM international Conference on Multimedia (Ottawa, Canada, September 30 - October 05, 2001). MULTIMEDIA '01, vol. 9. ACM, New York, NY, 107-118. DOI= http://doi.acm.org/10.1145/500141.500159

  • Jeon, J., Lavrenko, V., and Manmatha, R. 2003. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the 26th Annual international ACM SIGIR Conference on Research and Development in informaion Retrieval (Toronto, Canada, July 28 - August 01, 2003). SIGIR '03. ACM, New York, NY, 119-126. DOI= http://doi.acm.org/10.1145/860435.860459

  • M. La Cascia, S. Sethi, S. Sclaroff, "Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web," Content-Based Access of Image and Video Libraries, IEEE Workshop on, p. 24, IEEE Workshop on Content - Based Access of Image and Video Libraries, 1998

  • Yong Rui, Thomas S. Huang, Shih-Fu Chang, Image Retrieval: Current Techniques, Promising Directions, and Open Issues, Journal of Visual Communication and Image Representation, Volume 10, Issue 1, 1 March 1999, Pages 39-62, ISSN 1047-3203, DOI: 10.1006/jvci.1999.0413.

  • Henning Muller, Nicolas Michoux, David Bandon, Antoine Geissbuhler, A review of content-based image retrieval systems in medical applications--clinical benefits and future directions, International Journal of Medical Informatics, Volume 73, Issue 1, February 2004, Pages 1-23, ISSN 1386-5056, DOI: 10.1016/j.ijmedinf.2003.11.024 [Leonardo Martinez y Jose Alvarez]

  • David McG. Squire, Wolfgang Muller, Henning Muller, Thierry Pun, Content-based query of image databases: inspirations from text retrieval, Pattern Recognition Letters, Volume 21, Issues 13-14, Selected Papers from The 11th Scandinavian Conference on Image, December 2000, Pages 1193-1198, ISSN 0167-8655, DOI: 10.1016/S0167-8655(00)00081-7.

  • Chuang, W. T. and Yang, J. 2000. Extracting sentence segments for text summarization: a machine learning approach. InProceedings of the 23rd Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Athens, Greece, July 24 - 28, 2000). SIGIR '00. ACM, New York, NY, 152-159. DOI= http://doi.acm.org/10.1145/345508.345566 [Isabel Rojas]

  • David West, Neural network credit scoring models, Computers & Operations Research, Volume 27, Issues 11-12, September 2000, Pages 1131-1152, ISSN 0305-0548, DOI: 10.1016/S0305-0548(99)00149-5.

  • Baoan Yang, Ling X Li, Hai Ji, Jing Xu, An early warning system for loan risk assessment using artificial neural networks, Knowledge-Based Systems, Volume 14, Issues 5-6, August 2001, Pages 303-306, ISSN 0950-7051, DOI: 10.1016/S0950-7051(01)00110-1.

  • Sarlija, N., Bensic, M., and Zekic-Susac, M. 2006. A neural network classification of credit applicants in consumer credit scoring. In Proceedings of the 24th IASTED international Conference on Artificial intelligence and Applications(Innsbruck, Austria, February 13 - 16, 2006). V. Deved, Ed. International Association Of Science And Technology For Development. ACTA Press, Anaheim, CA, 205-210.

  • Kunal Verma, Amit Sheth, "Semantically Annotating a Web Service," IEEE Internet Computing, pp. 83-85, March/April, 2007 [Cristian Acosta, Juan David Guarnizo]