Abstract
Business Intelligence offers an interdisciplinary approach (within which is Information Systems), that taking all available information resources and using of analytical and synthesis tools with the ability to transform information into knowledge, focuses on generating knowledge that contributes to the management decision-making and generation of strategic plans in organizations. Information Mining is the sub-discipline of information systems which supports business intelligence tools to transform information into knowledge. It has defined as the search for interesting patterns and important regularities in large bodies of information. We address the need to identify information mining processes to obtain knowledge from available information. When information mining processes are defined, we may decide which data mining algorithms will support the information mining processes. In this context, this paper proposes a characterization of the information mining process related to the following business intelligence problems: discovery of rules of behavior, discovery of groups, discovery of significant attributes, discovering rules of group membership and weight of rules of behavior or rules of group memberships.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Thomsen, E.: BI’s Promised Land. Intelligent Enterprise 6(4), 21–25 (2003)
Negash, S., Gray, P.: Business Intelligence. In: Burstein, F., Holsapple, C. (eds.) En Handbook on Decision Support Systems 2, pp. 175–193. Springer, Heidelberg (2008)
Langseth, J., Vivatrat, N.: Why Proactive Business Intelligence is a Hallmark of the Real-Time Enterprise: Outward Bound. Intelligent Enterprise 5(18), 34–41 (2003)
Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.: Business Process Intelligence. Computers in Industry 53(3), 321–343 (2004)
Michalski, R., Bratko, I., Kubat, M.: Machine Learning and Data Mining, Methods and Applications. John Wiley & Sons (1998)
Kononenko, I., Cestnik, B.: Lymphography Data Set. UCI Machine Learning Repository (1986), http://archive.ics.uci.edu/ml/datasets/Lymphography (Último acceso 29 de Abril del 2008)
Michalski, R.: A Theory and Methodology of Inductive Learning. Artificial Intelligence 20, 111–161 (1983)
Quinlan, J.: Learning Logic Definitions from Relations. Machine Learning 5, 239–266 (1990)
Kohonen, T.: Self-Organizing Maps. Springer (1995)
Heckerman, D., Chickering, M., Geiger, D.: Learning bayesian networks, the combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
Chen, M., Han, J., Yu, P.: Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering 8(6), 866–883 (1996)
Chung, W., Chen, H., Nunamaker, J.: A Visual Framework for Knowledge Discovery on the Web: An Empirical Study of Business Intelligence Exploration. Journal of Management Information Systems 21(4), 57–84 (2005)
Chau, M., Shiu, B., Chan, I., Chen, H.: Redips: Backlink Search and Analysis on the Web for Business Intelligence Analysis. Journal of the American Society for Information Science and Technology 58(3), 351–365 (2007)
Golfarelli, M., Rizzi, S., Cella, L.: Beyond data warehousing: what’s next in business intelligence? In: Proceedings 7th ACM international Workshop on Data Warehousing and OLAP, pp. 1–6 (2004)
Koubarakis, M., Plexousakis, D.: A Formal Model for Business Process Modeling and Design. In: Wangler, B., Bergman, L.D. (eds.) CAiSE 2000. LNCS, vol. 1789, pp. 142–156. Springer, Heidelberg (2000)
Britos, P., Jiménez Rey, E., García-Martínez, E.: Work in Progress: Programming Misunderstandings Discovering Process Based On Intelligent Data Mining Tools. In: Proceedings 38th ASEE/IEEE Frontiers in Education Conference (2008) (en prensa)
Kaufmann, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons (1990)
Grabmeier, J., Rudolph, A.: Techniques of Cluster Algorithms in Data Mining. Data Mining and Knowledge Discovery 6(4), 303–360 (2002)
Ferrero, G., Britos, P., García-Martínez, R.: Detection of Breast Lesions in Medical Digital Imaging Using Neural Networks. In: Debenham, J. (ed.) Professional Practice in Artificial Intelligence. IFIP, vol. 218, pp. 1–10. Springer, Boston (2006)
Britos, P., Cataldi, Z., Sierra, E., García-Martínez, R.: Pedagogical Protocols Selection Automatic Assistance. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS (LNAI), vol. 5027, pp. 331–336. Springer, Heidelberg (2008)
Britos, P., Grosser, H., Rodríguez, D., García-Martínez, R.: Detecting Unusual Changes of Users Consumption. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice II. IFIP, vol. 276, pp. 297–306. Springer, Boston (2008)
Britos, P., Felgaer, P., García-Martínez, R.: Bayesian Networks Optimization Based on Induction Learning Techniques. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice II. IFIP, vol. 276, pp. 439–443. Springer, Boston (2008)
Britos, P., Abasolo, M., García-Martínez, R., Perales, F.: Identification of MPEG-4 Patterns in Human Faces Using Data Mining Techniques. In: Proceedings 13th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2005, pp. 9–10 (2005)
Cogliati, M., Britos, P., García-Martínez, R.: Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice. IFIP, vol. 217, pp. 305–314. Springer, Boston (2006a)
Britos, P., Dieste, O., García-Martínez, R.: Requirements Elicitation in Data Mining for Business Intelligence Projects. In: Avison, D., Kasper, G.M., Pernici, B., Ramos, I., Roode, D. (eds.) Advances in Information Systems Research, Education, and Practice. IFIP, vol. 274, pp. 139–150. Springer, Boston (2008b)
Britos, P.: Processes of Information Mining based on Intelligent Systems. PhD thesis in Computer Science. School of Computing. Universidad Nacional de La Plata (2008) (in spanish), http://postgrado.info.unlp.edu.ar/Carrera/Doctorado/Tesis/Britos-Tesis%20
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Martínez, R., Britos, P., Rodríguez, D. (2013). Information Mining Processes Based on Intelligent Systems . In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-38577-3_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38576-6
Online ISBN: 978-3-642-38577-3
eBook Packages: Computer ScienceComputer Science (R0)