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http://dx.doi.org/10.9708/jksci.2012.17.1.019

A FCA-based Classification Approach for Analysis of Interval Data  

Hwang, Suk-Hyung (Dept. of Computer Engineering, SunMoon University)
Kim, Eung-Hee (Biomedical Knowledge Engineering Lab., Seoul National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.1, 2012 , pp. 19-30 More about this Journal
Abstract
Based on the internet-based infrastructures such as various information devices, social network systems and cloud computing environments, distributed and sharable data are growing explosively. Recently, as a data analysis and mining technique for extracting, analyzing and classifying the inherent and useful knowledge and information, Formal Concept Analysis on binary or many-valued data has been successfully applied in many diverse fields. However, in formal concept analysis, there has been little research conducted on analyzing interval data whose attributes have some interval values. In this paper, we propose a new approach for classification of interval data based on the formal concept analysis. We present the development of a supporting tool(iFCA) that provides the proposed approach for the binarization of interval data table, concept extraction and construction of concept hierarchies. Finally, with some experiments over real-world data sets, we demonstrate that our approach provides some useful and effective ways for analyzing and mining interval data.
Keywords
Classification; Interval Data; Formal Concept Analysis; Binarization;
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1 R. Cole, P. Eklund and D. Walker, "Using Conc eptual Scaling In Formal Concept Analysis For Knowledge And Data Discovery In Medical Texts," International Symposium on Knowledge Retrieval, Use, and Storage for Efficiency, pp.151-164, 1997.
2 Susanne Prediger, "Logical Scaling in Formal Concept Analysis," LNCS 1257, 1997.
3 Joachim H. Correia, "Relational Scaling and Databases," Proceedings of the 10th International Conference on Conceptual Structures, LNCS2393, 2002.
4 Lyamine Hedjazi and Joseph Aguilar-Martin and Marie-Veronique Le Lann, "Similarity-margin based feature selection for symbolic interval data," Pattern Recognition Letters, Vol.32, No.4, 2011.
5 De Carvalho, F.A.T., De Souza, R.M.C.R., Chavent, M., Lechevallier, Y., "Adaptive Hausdorff distances and dynamic clustering of symbolic interval data," Pattern Recognition 27, pp.167-179, 2006.   DOI   ScienceOn
6 Quevedo, J., Puig, V., Cembrano, G., Blanch, J., Aguilar, J., Saporta, D., Benito, G., Hedo, M., Molina, A., "Validation and reconstruction of flow meter data in the Barcelona water distribution network." Journal of Control Eng. Practice 18, pp.640-651, 2010.   DOI   ScienceOn
7 Pang-Ning Tan, Michael Steinbach, Vipin Kumar, "Introduction to Data Mining," Addison-Wesley, 2005.
8 Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y. & Sun, X. "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature." Decision Support Systems 50, pp.559-569, 2010.
9 V. S. Verykios and E. Bertino and I. N. Fovino and L. P. Provenza and Y. Saygin and Y. Theodoridis, State-of-the-art in privacy preserving data mining, ACM SIGMOD Record, Vol. 1, No. 33, 2004.
10 Clifton Phua and Vincent C. S. Lee and Kate Smit h-Miles and Ross W. Gayler, "A Comprehensive Survey of Data Mining-based Fraud Detection Research," Artificial Intelligence Review, May 2005.
11 Thabtah, Fadi Abdeljaber, "A review of associative classification mining," Knowledge Engineering Review, Vol.22, No.1. pp.37-65, 2007.   DOI   ScienceOn
12 Ruotsalainen, Laura, Data Mining Tools for Techn ology and Competitive Intelligence, ESPOO 2008, VTT Tiedotteita n Research Notes 2451, 2008.
13 Ganter, B., Wille, R., "Formal Concept Analysis: Mathematical foundations." Springer, 1999.
14 Gerd Stumme, "Hierarchies of Conceptual Scales," Proceedings of Workshop on Knowledge Acquisition, Modeling and Management (KAW'99), 1999.
15 J. Poelmans, P. Elzinga, S. Viaene, G. Dedene, "Formal Concept Analysis in Knowledge Discovery: A Survey," ICCS2010, pp.139-153, 2010.