IMS Manthan (The Journal of Mgt., Comp. Science & Journalism)

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Prediction of Facility Location Using Evolutionary Spatial Data Mining


Affiliations
1 Dept of MCA, Thiagarajar School of Management, Madurai, Tamilnadu, India
2 Indra Ganesan College of Engineering, Tirchirapalli, Tamilnadu, India
     

   Subscribe/Renew Journal


One of the features that distinguish the Geographical Information Systems (GIS) from other information systems is spatial information function. This function usually provides selection switches and solutions for GIS users. Simultaneously with GIS techniques development, the GIS executive analysis functions have also been developed interestingly. For instance, Facility location allocation is one of the areas of interest in GIS. The problem is evaluated using different methods of spatial data mining, with respect to the association analysis of the spatial data, clustering the associations and classifying them.

Keywords

Facility, Location, Allocation, Spatial Data, Mining, Multi Objective Genetic Algorithm, GIS
Subscription Login to verify subscription
User
Notifications
Font Size


  • Margaret H. Dunham and Sridhar, S. (2006), “Data Mining Introductory and Advanced Topics”, Pearson Education.

  • Yoo J.S., Shekhar, S. and Celik, M. (2005). “A Join-less Approach for Co-location Pattern Mining: A Summary of Results”, In 5th IEEE-ICDM, Houston, p.813-816. IEEE Computer Society.

  • Yoo, J.S. and Shekhar S. (2004) “A partial join approach for mining co-location patterns”, In 12th ACM-GIS, Washington, p.241-249, ACM Press.

  • Appice, M., Berardi, M., Ceci, M. and Malerba, D (2005), “Mining and Filtering Multi-level Spatial Association Ruleswith ARES”, In 15th ISMIS, New York, p.342-353. Springer.

  • Mennis, J. and Liu, J.W. (2005), “Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change”, Transactions in GIS, v9 (1), January, p. 5- 17.

  • Koperski, K. and Han, J. (1995), “Discovery of spatial association rules in geographic information databases”, In 4th SSD, Portland, p. 47-66. Springer.

  • Bogorny, V. (2006), “Enchancing spatial association rule mining in geographic databases”. PhD Thesis, Instituto de Informatica da UFRGS, Brazil, October.

  • www.dsi.unive.it/~dm/ssd95.pdf

  • Alex A. Freitas(2003), “A survey of evolutionary algorithms for data mining and knowledge discovery”, Advances in evolutionary computing: theory and applications, Springer-Verlag New York, Inc., New York, NY.

  • Dehuri, S., Jagadev, A. K., Ghosh A. And Mall R. (2006), “Multiobjective Genetic Algorithm for Association Rule Mining Using a Homogeneous Dedicated Cluster of Workstations”. American Journal of Applied Sciences 3 (11): 2086-2095, ISSN 1546-9239.

  • Peter P. Wakabi-Waiswa and Venansius Baryamureeba (2008), “Extraction of Interesting Association Rules Using Genetic Algorithms”. International Journal of Computing and ICT Research, Vol. 2, No. 1, pp. 26 – 33.

  • Colorni, A., Dorigo, M. and Maniezzo, V. (1991). “Positive feedback as a search strategy”. Technical Report No. 91-016, Politecnico di Milano, Italy,.

  • Colorni, A., Dorigo, M. and Maniezzo, V. (1991), “The ant system: an autocatatlytic process”. Technical Report No. 91- 016, Politecnico di Milano, Italy.

  • Dorigo, M. (1992), “Optimization, Learning and Natural Algorithms”. Ph.D. Thesis, Politecnico di Milano, Italy.

  • Beckers, R., Deneubourg J.L and Goss S. (1992), “Trails and Uturns in the selection of the shortest path by the ant lasius niger”. Journal of Theoretical Biology, 159, pp. 397- 415.

  • Goss, S., Aron, S., Deneubourg, J.L. and Pasteels, J.M. (1989),”Self-organized shortcuts in the argentine ant”, Naturwissenschaften, 76, pp. 579-581.

  • B. Holldobler and Wilson,E.O. (1990), “The Ants”, Springer-Verlag: Berlin,.

  • Waler A.Kosters, Elena Marchiori and Ard A.J. Oerlemans (1999), “Mining Clusters with Association Rules”, Advances in Intelligent Data Analysis (IDA-99) (D.J.Hand, J.N.Kok and M.R.Berthold, Eds.), Lecture Notes in Computer Science 1642, Springer, pp. 39-50.

  • Toivonen, H., Klemettinen, M., Ronkainen, P., Hatonen, K., and Mannila, H. (1995), “Pruning and grouping discovered association rules”, In Proceedings ECML-95 Workshop on Statistics, Machine Learning, and Knowledge Discovery in Database, April, pp 47-52.

  • Pi Dechang and Qin Xiaolin(2008), “A new Fuzzy Clustering algorithm on Association rules for knowledge management”, Information Technology Journal 7(1), pp. 119-124.

  • Alipio Jorge(2004), “Hierarchical Clustering for thematic browsing and summarization of large sets of Association Rules”, In Proceedings of the 4th SIAM International Conference on Data Mining, Orlando, FL, pp. 178-187.

  • Li, G. and Hamilton, H. J. (2004), “Basic association rules”, In Proceedings of the 4th SIAM International Conference on Data Mining, Orlando, FL, pp. 166–177.

  • Li, W., Han, J. and Pei, J. (2001), “CMAR: accurate and efficient classification based on multiple class-association rules”, International Proceedings of IEEE International Conference on Data Mining (ICDM2001), pp. 369–376.

  • Thabtah, A., and Cowling, P. I. (2007), “A greedy classification algorithm based on association rule”, Applied Soft Computing, Vol. 7, No. 3, pp. 1102-1111, June.

  • Adriano Veloso, Wagner Meira, Marcos Gonçalves, and Mohammed Zaki(2007), “Multi-label Lazy Associative Classification”, Knowledge Discovery in Databases: PKDD 2007, pp. 605-612.

  • Diansheng Guo, Jeremy Mennis (2009), “Spatial data mining and geographic knowledge discovery – An introduction”, Computers, Environment and Urban Systems 33, 403–408.

  • Ester, M., Kriegel, H. P., & Sander, J. (1997), “Spatial data mining: A database approach”, Advances in spatial databases. Berlin: Springer-Verlag, pp. 47–66, Berlin.

  • Koperski, K., Han, J., and Stefanovic, N.( 1998), “ An efficient two-step method for classification of spatial data” , 1998 international symposium on spatial data handling SDH’98, pp. 45–54, Vancouver, BC, Canada.

  • Malerba, D., Esposito, F., Lanza, A., Lisi, F.A. and Appice, A. (2003): “Empowering a GIS with Inductive Learning Capabilities: The Case of INGENS”. Journal of Computers, Environment and Urban Systems, Elsevier Science, 27. 265-281.

  • Nadia Ghamrawi , Andrew McCallum (2005), “Collective multilabel classification”, Proceedings of the 14th ACM international conference on Information and knowledge management, Bremen, Germany, October 31-November 05.

  • Benhui Chen, Liangpeng Ma and Jinglu Hu (2010), “An improved multi-label classification method based on svm with delicate decision boundary“, International Journal of Innovative Computing, Information and Control , Volume 6, Number 4, pp. 1605–1614, April.

  • Tsoumakas, G., Katakis, I. and Vlahavas, I. (2009), “Mining Multi-label Data.”, Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Heidelberg.

  • Brinker, K., Furnkranz, J. and Hullermeier, E. (2006), “A unified model for multilabel classification and ranking”, in Proceedings of the 17th European Conference on Artificial Intelligence (ECAI '06), Riva del Garda, Italy, pp. 489-493.

  • Min-Ling Zhang and Zhi-Hua Zhou (2007), “Ml-knn: A lazy learning approach to multi-label learning,” Pattern Recognition, vol. 40, no. 7, pp. 2038–2048.

  • Weiwei Cheng and Eyke Hllermeier (2009), “Combining instance based learning and logistic regression for multilabel classification,” Machine Learning, vol. 76, no. 2-3, pp. 211–225, September.

  • Fayyad, U., Piatetsky-Shapiro, G., Smith, G. and Uthurusamy (1998), R. Advances in Knowledge Discovery and Data Mining, Menlo Park, CA: AAAI Press.

  • Thabtah F., Cowling P., and Peng Y. (2004), “Multi-label Classification Learning”, Proceedings of the IEEE 2004 International Conference on Advances in Intelligent Systems (AISTA ’04), Luxembourg, Luxembourg, pp. 207-213, Nov.

  • W. Li, J. Han, and J. Pei. (2001), “CMAR: Accurate and efficient classification based on multiple-class association rule”, in Proceeding of the First IEEE International Conference on Data Mining (ICDM’01), pp. 369- 376, San Jose, CA, Nov.

  • Yin, X. and Han, J. (2003), CPAR: Classification Based on Predictive Association Rules, Proceedings SIAM International Conferene on Data Mining (SDM'03), 331-335.

  • Zhu, Xiaojin(2008). “Semi-Supervised Learning Literature Survey”, Computer Sciences, Univeristy of Wisconsin-Madison. Technical Report 1530, University of Wisconsin – Madison,.

  • Seeger M. (2002), “Learning with labeled and unlabeled data”, Technical Report, University of Edinburgh, Institute for Adaptive and Neural Computation, pp. 1-62 ,Dec.

  • Kemp T., Waibel A. (1966), “Unsupervised Training of a Speech Recognizer: Recent Experiments”, Proc. Eurospeech, vol. 6, pp. 2725- 2728, 1999.Nagy G., Shelton G.L., “Self-corrective character recognition systems”, IEEE Trans. On Information Theory, IT-12, no. 2, pp. 215-222, April.

  • G. Nagy (2004), “Classifiers that improve with use”, Proceedings of Conference on Pattern Recognition and Multimedia, IEICE, Tokyo, pp. 79-86, February.

  • Inoue M., Ueda N. (2003), “Exploitation of unlabeled sequences in hidden markov models”, IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, pp. 1570-1581, Dec.

  • Young T.Y., Farjo A. (1972), “On decision directed estimation and stochastic approximation”, IEEE Transactions On Information Theory, pp. 671-673, Sep.

  • Dempster A.P., Laird N.M., Rubin D.B.( 1977), “Maximum likelihood from incomplete data via the EM algorithm”, Journal of the Royal Statistical Society, Series B, 39(1), pp. 1-38,.

  • Nigam K., McCallum A.K. (2000), Thrun S., Mitchell T., “Text classification from labeled and unlabeled documents using EM”, Machine Learning, 39, pp. 103-134,.

  • Blum A., Mitchell T. (1998), “Combining labeled and unlabeled data with co-training”, Proceedings of the Workshop on Computational Learning Theory, pp. 92-100.

  • Seeger M. (2002), “Learning with labeled and unlabeled data”, Technical Report, University of Edinburgh, Institute for Adaptive and Neural Computation, pp. 1-62, Dec.

  • Nigam K, Ghani R. (2000), “Analyzing the effectiveness and applicability of co-training”, Proceedings 5th International Conference on Information and Knowledge Management, pp. 86-93.

  • Goldman S., Zhou Y. (2000), “Enhancing supervised learning with unlabeled data”, Proceedings of the 7th International Conference On Machine Learning, ICML 2000, pp. 327-334.

  • Roli, F. (2005), “Semi-Supervised Multiple Classifier Systems: Background and Research Directions”, Multiple Classifier Systems, Springer Verlag, Vol. 3541/2005, pp 674-676.


Abstract Views: 219

PDF Views: 0




  • Prediction of Facility Location Using Evolutionary Spatial Data Mining

Abstract Views: 219  |  PDF Views: 0

Authors

J. Arunadevi
Dept of MCA, Thiagarajar School of Management, Madurai, Tamilnadu, India
V. Rajamani
Indra Ganesan College of Engineering, Tirchirapalli, Tamilnadu, India

Abstract


One of the features that distinguish the Geographical Information Systems (GIS) from other information systems is spatial information function. This function usually provides selection switches and solutions for GIS users. Simultaneously with GIS techniques development, the GIS executive analysis functions have also been developed interestingly. For instance, Facility location allocation is one of the areas of interest in GIS. The problem is evaluated using different methods of spatial data mining, with respect to the association analysis of the spatial data, clustering the associations and classifying them.

Keywords


Facility, Location, Allocation, Spatial Data, Mining, Multi Objective Genetic Algorithm, GIS

References