Searching for true information with spatial data mining
##plugins.themes.bootstrap3.article.details##
Abstract
Information is a key element of organizational processes. In recent years, the technology has had an accelerated growth, in line with the increase of what are regarded as useful and necessary tools to facilitate and expedite those processes giving an added value to productivity. Business intelligence has been defined as the transforming of data into knowledge, providing decision-making support at the strategic and tactical level where and when appropriate, providing a competitive advantage and increasing the effectiveness. One of the tools that has become useful for the exploration of data is On-line Analytical Processing (OLAP), which allows to obtain outstanding data among quantities of information, but it is faulty for the analysis of geographical data, for which SOLAP has arisen, which offers methods of special treatment for space data. Data mining has been adapted within companies, with the purpose of carrying out exploration and analysis of data focused on the discovery of knowledge. Because of the important place that space information is occupying nowadays, the spatial data mining has arisen. This process allows us to discover useful and unexpected patterns inside the data. The techniques of spatial data mining are applied to extract knowledge, starting from large volumes of data, which can be of space and non-space types. Among them are generalization, grouping, and space association.
Keywords
Data mining, business intelligence, OLAP technologyMinería de datos, inteligencia de negocio, tecnología OLAP
References
ABRIL, D. y PÉREZ, J. Estado actual de las tecnologías de bodega de datos y OLAP aplicadas a bases de datos espaciales. Revista Ingeniería de Investigación. 2007, vol. 27, núm.1, pp. 58-67. ISSN 0120-5609.
AGRAWAL, R.; IMIELINSKI, T. y SWAMI, A. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. 1993, pp. 1-4.
AL-HAMAMI, A.; MOHAMMAD, A. y HASSAN, S. Applying data mining techniques in intrusion detection system on web and analysis of web usage. Information Technology Journal. 2006, vol. 5, núm. 1, pp. 1-4.
ASSAF, S.; RAN, W. y DAN, T. A high-performance distributed algorithm for mining association rules. In The Third IEEE International Conference on Data Mining (ICDM’03). Melbourne, 2003.
BALTZER, O. Spatial OLAP and data mining. 2006 [web en línea].. [Consulta: 15-04-2008].
BÉDARD, Y.; PROULX, M. J. y RIVEST, S. Enrichissement du OLAP pour l’analyse géographique: exemples de réalisation et différentes possibilités technologiques. En BENTAYEB,
F.; BOUSSAID, O.; DARMONT, J. y RABASEDA, S. (Eds.). Entrepôts de Données et Analyse en ligne, RNTI B_1. Paris: Cépaduès, 2005, pp. 1-20.
BOHÓRQUEZ, J. E. Aproximación metodológica de un Spatial Data Warehouse. 2000 [documento en línea]. < http://proceedings.esri.com/library/userconf/latinproc00/colombia/spatial_data.pdf>. [Consulta 20-04-2009].
BRAMER, M. Date for data mining. En Principles of data mining. London: Springer, 2007, pp. 11-20.
CBR. Spatial business intelligence. 2005 [web en línea].. [Consulta: 15-04-2008].
CHEUNGI, D. et al. Maintenance of discovered association rules in large databases: an incremental updating technique. Proceedings of ICDE. 1996, pp. 1-3.
DOMINGO, P. y LOWD, D. Naive bayes models for probability estimation. ACM International Conference Proceeding Series. 2005, vol. 119, pp. 529-536.
EUN-JEONG, S. et al. A spatial data mining method by clustering analysis. Proceedings of the 6th International Symposium on Advances in Geographic Information Systems. Washington, 6-7 de noviembre de 1998.
GIRALDO, R. Análisis exploratorio de variables regionalizadas con métodos funcionales. Revista Colombiana de Estadística. 2007, vol. 30, núm. 1, pp.115-127.
GONZALES, M. Spatial business intelligence. The spatial & visual components for effective BI Documento en línea]. 2004. [Consulta: 10-05-2008].
HAN, J.; KAMBER, M. y TUNG, A. Spatial clustering methods in data mining: a survey. En MILLER, H. y HAN, J. Geographic data mining and knowledge discovery. London: Taylor and Francis, 2001.
HAN, J. y KAMBER, M. Data mining: Concepts and techniques. 7th. ed. Morgan Kaufmann, 2006.
HARMS, S. K.; DEOGUN, J. y GODDARD, S. Building knowledge discovery into a geo-spatial decision support system. Proceedings of the 2003 ACM symposium on Applied Computing, 2003, pp. 445-449.
HERNÁNDEZ, J. et al. Parte 3: Técnica de minería de datos. En Introducción a la minería de datos. New York: Pearson Prentice Hall, 2007, pp. 281-351.
HSU, W.; LI, M. y WANG, J. Parte 1: Spatial data mining introduction. Temporal and spatiotemporal data mining. Hershey: Igi Publishing, 2008, pp. 1-10.
INMON, W. H. Parte 2. The data warehouse environment. En Building data warehouse. 4th ed. Indianapolis: Wiley, 2005, pp. 9-46.
KURGAN, L. y MUSILEK, P. A survey of knowledge discovery and data mining process models. The Knowledge Engineering Review. 2006, vol. 21, núm. 1, pp. 1-24.
LUTU, P. An integrated approach for scaling up classification and prediction algorithms for data mining. ACM International Conference Proceeding Series, 2002, vol. 30, pp. 110-117.
MALINOWSKI, E. y ZIMÁNKY, E. Parte 2. Introduction to databases and data warehouse. En Advanced data warehouse design: from conventional to spatial and temporal applications. Berlin: Springer, 2008, pp. 16-51.
—. Representing spatiality in a conceptual multidimensional model. Proceedings of the 12th ACM Int. Symp. on Advances in Geographic Information Systems, ACM GIS, 2004, pp. 12-21.
MAN, L. Y. y NIKOS, M. Clustering objects on a spatial network. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, 2004.
MARCANO, A.; YELITZA, J. y TALAVERA, R. Minería de datos como soporte a la toma de decisiones empresariales. Revista de Ciencias Humanas y Sociales, 2006, núm. 52, pp. 104-118.
MARTÍN SÁNCHEZ, F.; LÓPEZ CAMPOS, G. y MAOJO GARCÍA, V. Bioinformática y salud: impactos de la aplicación de las nuevas tecnologías para el tratamiento de la información genética en la investigación biomédica y la práctica clínica. Informática y Salud. 1999 [web en línea].. [Consulta: 22-03-2008].
MARTIN, E.; KRIEGEL, H.-P. y SANDER, J. Algorithms and applications for spatial data mining. In MILLER, H. y HAN, J. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001. pp. 1-10.
MARTÍNEZ DE PISÓN, F. et al. Minería de datos en series temporales para la búsqueda de conocimiento oculto en históricos de procesos industriales. I Congreso Español de Informática, Granada, 13-16 de septiembre de 2005.
MATIAS, R. y MOURA-PIRES, J. Spatial On-Line Analytical Processing (SOLAP): A tool the to analyze the emission of pollutants in industrial installations. 2005 [documento en línea].. [Consulta: 30-03-2008].
MENNIS, J. y LIU, J. W. Mining association rules in spatio-temporal data: an analysis of urban socioeconomic and land cover change. Transactions in GIS, 2005, pp. 1-17.
MORENO, M. et al. Aplicación de técnicas de minería de datos en la construcción y validación de modelos predictivos y asociativos a partir de especificaciones de requisitos de software. 2001 [documento en línea].. [Consulta: 25-03-2008]
MOSS, L. y ATRE, S. Guide to the development steps. En Business intelligence roadmap: the complete project lifecycle for decision-support applications. New York: Addison Wesley, 2003. 0-201-78420-3.
OLMO, J. y MARTÍNEZ, T. Aplicación de la teoría de variables regionalizadas en la investigación de marketing. Revista Europea de Dirección y Economía de la Empresa. 1992, vol. 1, núm. 1, pp. 125-132.
OLSON, D. y DENLE, D. Parte 2. Data mining methods and tools. Técnicas avanzadas de minería de datos. Berlin: Springer, 2008. pp. 39-144.
PING-YU, H.; YEN-LIANG, C. y CHUN-CHING, L. Algorithms for mining association rules in bag databases. Information Sciences. 2004, vol. 166, pp. 31-35.
RAYMOND T. N. y HAN, J. CLARANS: a method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering. 2002, vol. 14, núm. 5, pp. 1004-1006.
—. Efficient and effective clustering methods for spatial data mining. Proceedings of the 20th International Conference on Very Large Data Bases: September 12-15, 1994.
REINSCHMIDT, J. y FRANCOISE, A. Business intelligence certification guide [Libro en línea]. IBM Redbooks, 2000.. [Consulta: 25-03-2008]. ISBN e-book 0738415111.
RIVEST, S. et al. SOLAP technology: merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data. 2005 [Documento en línea].. [Consulta: 02-04-08].
—. SOLAP: a new type of user interface to support spatio-temporal multidimensional data exploration and analysis. 2003 [documento en línea].. [Consulta: 07-04-08].
RODDICK, J. y LEES, B. Paradigms for spatial and spatio-temporal data mining. In MILLER, H. y HAN, J. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001.
RODDICK, J. F. y SPILIOPOULOU, M. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and data engineering. 2002, vol.14, núm. 4, pp. 750-767.
SÁNCHEZ, D.; MIRANDA, M. y CERDA, L. Reglas de asociación aplicadas a la detección de fraude con tarjetas de crédito. En Actas del XII Congreso Español sobre Tecnologías y Lógica Fuzzy, Jaén, 15-17 de septiembre de 2004.
SANTOS, M. y AMARAL, L. Knowledge discovery in spatial databases: the qualitative approach. 2000 [documento en línea].. [Consulta: 10-04-2008].
SAS. ESRI offer solutions for GIS and business intelligence needs. 2004 [web en línea].. [Consulta: 05-05-2008].
SHEKHAR, S.; ZHANG, P.; HUANG YAN, R. y VATSAVAI R. R. Trends in spatial data mining. En KARGUPTA, H. y JOSHI, A. (Eds.). Data mining: next generation challenges and future directions. AAAI/MIT Press, 2008, pp. 357-380.
SVETLOZAR, N. Mining qualified association rules in distributed databases. En Workshop on data mining and exploration middleware for distributed and grid computing. Minneapolis: University of Minnesota, 2003.
VYAS, R.; KUMAR, L. y TIWARY, U. Exploring spatial ARM (Spatial Association Rule Mining) for geo-decision support system. Journal of Computer Science. 2007, vol. 3, núm. 11, pp. 1-3.
WREMBEL, R. y KONCILIA, C. Parte 1. Modeling and designing. En Data warehouse and OLAP: concepts, architectures and solutions. New York: IRM Press, 2007, pp. 1-58.
AGRAWAL, R.; IMIELINSKI, T. y SWAMI, A. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. 1993, pp. 1-4.
AL-HAMAMI, A.; MOHAMMAD, A. y HASSAN, S. Applying data mining techniques in intrusion detection system on web and analysis of web usage. Information Technology Journal. 2006, vol. 5, núm. 1, pp. 1-4.
ASSAF, S.; RAN, W. y DAN, T. A high-performance distributed algorithm for mining association rules. In The Third IEEE International Conference on Data Mining (ICDM’03). Melbourne, 2003.
BALTZER, O. Spatial OLAP and data mining. 2006 [web en línea].
BÉDARD, Y.; PROULX, M. J. y RIVEST, S. Enrichissement du OLAP pour l’analyse géographique: exemples de réalisation et différentes possibilités technologiques. En BENTAYEB,
F.; BOUSSAID, O.; DARMONT, J. y RABASEDA, S. (Eds.). Entrepôts de Données et Analyse en ligne, RNTI B_1. Paris: Cépaduès, 2005, pp. 1-20.
BOHÓRQUEZ, J. E. Aproximación metodológica de un Spatial Data Warehouse. 2000 [documento en línea]. < http://proceedings.esri.com/library/userconf/latinproc00/colombia/spatial_data.pdf>. [Consulta 20-04-2009].
BRAMER, M. Date for data mining. En Principles of data mining. London: Springer, 2007, pp. 11-20.
CBR. Spatial business intelligence. 2005 [web en línea].
CHEUNGI, D. et al. Maintenance of discovered association rules in large databases: an incremental updating technique. Proceedings of ICDE. 1996, pp. 1-3.
DOMINGO, P. y LOWD, D. Naive bayes models for probability estimation. ACM International Conference Proceeding Series. 2005, vol. 119, pp. 529-536.
EUN-JEONG, S. et al. A spatial data mining method by clustering analysis. Proceedings of the 6th International Symposium on Advances in Geographic Information Systems. Washington, 6-7 de noviembre de 1998.
GIRALDO, R. Análisis exploratorio de variables regionalizadas con métodos funcionales. Revista Colombiana de Estadística. 2007, vol. 30, núm. 1, pp.115-127.
GONZALES, M. Spatial business intelligence. The spatial & visual components for effective BI Documento en línea]. 2004.
HAN, J.; KAMBER, M. y TUNG, A. Spatial clustering methods in data mining: a survey. En MILLER, H. y HAN, J. Geographic data mining and knowledge discovery. London: Taylor and Francis, 2001.
HAN, J. y KAMBER, M. Data mining: Concepts and techniques. 7th. ed. Morgan Kaufmann, 2006.
HARMS, S. K.; DEOGUN, J. y GODDARD, S. Building knowledge discovery into a geo-spatial decision support system. Proceedings of the 2003 ACM symposium on Applied Computing, 2003, pp. 445-449.
HERNÁNDEZ, J. et al. Parte 3: Técnica de minería de datos. En Introducción a la minería de datos. New York: Pearson Prentice Hall, 2007, pp. 281-351.
HSU, W.; LI, M. y WANG, J. Parte 1: Spatial data mining introduction. Temporal and spatiotemporal data mining. Hershey: Igi Publishing, 2008, pp. 1-10.
INMON, W. H. Parte 2. The data warehouse environment. En Building data warehouse. 4th ed. Indianapolis: Wiley, 2005, pp. 9-46.
KURGAN, L. y MUSILEK, P. A survey of knowledge discovery and data mining process models. The Knowledge Engineering Review. 2006, vol. 21, núm. 1, pp. 1-24.
LUTU, P. An integrated approach for scaling up classification and prediction algorithms for data mining. ACM International Conference Proceeding Series, 2002, vol. 30, pp. 110-117.
MALINOWSKI, E. y ZIMÁNKY, E. Parte 2. Introduction to databases and data warehouse. En Advanced data warehouse design: from conventional to spatial and temporal applications. Berlin: Springer, 2008, pp. 16-51.
—. Representing spatiality in a conceptual multidimensional model. Proceedings of the 12th ACM Int. Symp. on Advances in Geographic Information Systems, ACM GIS, 2004, pp. 12-21.
MAN, L. Y. y NIKOS, M. Clustering objects on a spatial network. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, 2004.
MARCANO, A.; YELITZA, J. y TALAVERA, R. Minería de datos como soporte a la toma de decisiones empresariales. Revista de Ciencias Humanas y Sociales, 2006, núm. 52, pp. 104-118.
MARTÍN SÁNCHEZ, F.; LÓPEZ CAMPOS, G. y MAOJO GARCÍA, V. Bioinformática y salud: impactos de la aplicación de las nuevas tecnologías para el tratamiento de la información genética en la investigación biomédica y la práctica clínica. Informática y Salud. 1999 [web en línea].
MARTIN, E.; KRIEGEL, H.-P. y SANDER, J. Algorithms and applications for spatial data mining. In MILLER, H. y HAN, J. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001. pp. 1-10.
MARTÍNEZ DE PISÓN, F. et al. Minería de datos en series temporales para la búsqueda de conocimiento oculto en históricos de procesos industriales. I Congreso Español de Informática, Granada, 13-16 de septiembre de 2005.
MATIAS, R. y MOURA-PIRES, J. Spatial On-Line Analytical Processing (SOLAP): A tool the to analyze the emission of pollutants in industrial installations. 2005 [documento en línea].
MENNIS, J. y LIU, J. W. Mining association rules in spatio-temporal data: an analysis of urban socioeconomic and land cover change. Transactions in GIS, 2005, pp. 1-17.
MORENO, M. et al. Aplicación de técnicas de minería de datos en la construcción y validación de modelos predictivos y asociativos a partir de especificaciones de requisitos de software. 2001 [documento en línea].
MOSS, L. y ATRE, S. Guide to the development steps. En Business intelligence roadmap: the complete project lifecycle for decision-support applications. New York: Addison Wesley, 2003. 0-201-78420-3.
OLMO, J. y MARTÍNEZ, T. Aplicación de la teoría de variables regionalizadas en la investigación de marketing. Revista Europea de Dirección y Economía de la Empresa. 1992, vol. 1, núm. 1, pp. 125-132.
OLSON, D. y DENLE, D. Parte 2. Data mining methods and tools. Técnicas avanzadas de minería de datos. Berlin: Springer, 2008. pp. 39-144.
PING-YU, H.; YEN-LIANG, C. y CHUN-CHING, L. Algorithms for mining association rules in bag databases. Information Sciences. 2004, vol. 166, pp. 31-35.
RAYMOND T. N. y HAN, J. CLARANS: a method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering. 2002, vol. 14, núm. 5, pp. 1004-1006.
—. Efficient and effective clustering methods for spatial data mining. Proceedings of the 20th International Conference on Very Large Data Bases: September 12-15, 1994.
REINSCHMIDT, J. y FRANCOISE, A. Business intelligence certification guide [Libro en línea]. IBM Redbooks, 2000.
RIVEST, S. et al. SOLAP technology: merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data. 2005 [Documento en línea].
—. SOLAP: a new type of user interface to support spatio-temporal multidimensional data exploration and analysis. 2003 [documento en línea].
RODDICK, J. y LEES, B. Paradigms for spatial and spatio-temporal data mining. In MILLER, H. y HAN, J. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001.
RODDICK, J. F. y SPILIOPOULOU, M. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and data engineering. 2002, vol.14, núm. 4, pp. 750-767.
SÁNCHEZ, D.; MIRANDA, M. y CERDA, L. Reglas de asociación aplicadas a la detección de fraude con tarjetas de crédito. En Actas del XII Congreso Español sobre Tecnologías y Lógica Fuzzy, Jaén, 15-17 de septiembre de 2004.
SANTOS, M. y AMARAL, L. Knowledge discovery in spatial databases: the qualitative approach. 2000 [documento en línea].
SAS. ESRI offer solutions for GIS and business intelligence needs. 2004 [web en línea].
SHEKHAR, S.; ZHANG, P.; HUANG YAN, R. y VATSAVAI R. R. Trends in spatial data mining. En KARGUPTA, H. y JOSHI, A. (Eds.). Data mining: next generation challenges and future directions. AAAI/MIT Press, 2008, pp. 357-380.
SVETLOZAR, N. Mining qualified association rules in distributed databases. En Workshop on data mining and exploration middleware for distributed and grid computing. Minneapolis: University of Minnesota, 2003.
VYAS, R.; KUMAR, L. y TIWARY, U. Exploring spatial ARM (Spatial Association Rule Mining) for geo-decision support system. Journal of Computer Science. 2007, vol. 3, núm. 11, pp. 1-3.
WREMBEL, R. y KONCILIA, C. Parte 1. Modeling and designing. En Data warehouse and OLAP: concepts, architectures and solutions. New York: IRM Press, 2007, pp. 1-58.
How to Cite
Dueñas-Reyes, M. X. (2009). Searching for true information with spatial data mining. Ingenieria Y Universidad, 13(1). Retrieved from https://revistas.javeriana.edu.co/index.php/iyu/article/view/953
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.