International Journal of Information Technology and Computer Science(IJITCS)
ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)
Published By: MECS Press
IJITCS Vol.5, No.12, Nov. 2013
Improving Situational Awareness for Precursory Data Classification using Attribute Rough Set Reduction Approach
Full Text (PDF, 280KB), PP.47-55
The task of modeling the distribution of a large number of earthquake events with frequent tremors detected prior to a main shock presents us unique challenges to model a robust classifier tool for rapid responses are needed in order to address victims. We have designed using a relational database for running a geophysical modeling application after connecting database record of all clusters of foreshock events from (1998-2010) for a complete catalog of seismicity analysis for the Himalayan basin. by Nath et al,2010. This paper develops a reduced rough set analysis method and implements this novel structure and reasoning process for foreshock cluster forecasting. In this study, we developed a reusable information technology infrastructure, called Efficient Machine Readable for Emergency Text Selection(EMRETS). The association and importance of precursory information in reference to earthquake rupture analysis is found out through attribute reduction based on rough set analysis. Secondly, find the importance of attributes through information entropy is a novel approach for high dimensional complex polynomial problems pre-dominant in geo-physical research and prospecting. Thirdly, we discuss the reducible indiscernible matrix and decision rule generation for a particular set of geographical co-ordinates leading to the spatial discovery of future earthquake having prior foreshock. This paper proposes a framework for extracting, classifying, analyzing, and presenting semi-structured catalog data sources through feature representation and selection.
Cite This Paper
Pushan Kumar Dutta, O. P. Mishra, M.K.Naskar,"Improving Situational Awareness for Precursory Data Classification using Attribute Rough Set Reduction Approach", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.12, pp.47-55, 2013. DOI: 10.5815/ijitcs.2013.12.06
Bird, S., Ewan K, Edward L. Natural language processing with Python. O'Reilly Media, 2009.
Dehbozorgi, L.; Farokhi, F., “Effective feature selection for short-term earthquake prediction using Neuro-Fuzzy classifier “,Centran Tehran Branch, Sci. Assoc. of Electr. & Electron. Eng., Islamic Azad Univ.,2008.
Xing, Z., Pei,J., Dong,G. and Philip S. Yu. Mining sequence classifiers for early prediction.In: Proceedings of the 2008 SIAM international conference on data mining (SDM’08), Atlanta, GA, pp. 24-26. 2008.
Aydin, I., M. Karakose, and E. Akin. The Prediction Algorithm Based on Fuzzy Logic Using Time Series Data Mining Method." World Academy of Science, Engineering and Technology 51 (2009): 91-98.
Dzwinel, W. Blasiak.J.,Method of particles in visual clustering of multi-dimensional and large data sets. Future Generation Computer Systems 15.3 (1999): 365-379.
Iftikhar U. S., Munakata,T.,Application of rough set and decision tree for characterization of premonitory factors of low seismic activity ,Expert Systems with Applications, Volume 36(1),2009.
Mishra O.P.,(2004), Lithospheric heterogeneity and seismotectonics of NE Japan Forearc and Indian regions, unpublished D.Sc. thesis, Ehime University, Japan, 223p.
Mishra O. P. and Zhao, D., Crack density, saturation rate and porosity at the 2001 Bhuj, India, earthquake hypocenter: a fluid-driven earthquake? Earth Planet. Sci. Lett., 212, 393 – 405, 2003.
Mishra, O. P., Umino, N., and Hasegawa, A., Tomography of northeast Japan forearc and its implications for interpolate seismic coupling. Geophys. Res. Lett., 30, doi; 10.1029/2003GL017736, 2003.
Dumais, S., Platt, J., Heckerman, D. & Sahami, M., Inductive learning algorithms and representations for text categorization. In CIKM’98, 1998.
Fuernkranz, J., Mitchell, T. & Riloff, E., A case study in using linguistic phrases for text categorization on the WWW. Learning for Text Categorization.,2000, AAAI Press.
Kumaran, G. & Allan, J.,Text classification and named entities for new event detection.,2004 In:SIGIR’04.
Jackson, P. & Moulinier, I. (2007). “Natural Language Processing for Online applications: text retrieval, extraction and categorization”. John Benjamin’s Publishing Co, second edition.
Stevenson M. & Greenwood M. A. Comparing Information Extraction Pattern Models, In: Proceedings of the Workshop on Information Extraction Beyond The Document, Association for Computational Linguistics, Sydney,2006, pp.12-19.
Turno, J.,Information Extraction, Multilinguality and Portability. Revista Iberoamericana de Inteligencia Artificial, 2003,No. 22, pp. 57-78.
How is sqlite different <http:// www. sqlite. Org /different.html> accessed 15th July,2011.
Bouckaert, R. Low level information extraction”. 2002,In: Proceedings of the workshop on Text Learning , Sydney, Australia.,1992.
Hobbs, J. R. The Generic Information Extraction System. In: B. Sundheim, editor. Fourth Message Understanding Conference (MUC-4), Mc Lean, Virginia Distributed by Morgan Kauffman Publishers, Inc., San Mateo, California,2002.
Muslea, I. (1999). “Extraction Patterns for Information Extractions Tasks: A Survey”. In Proceedings of the AAAI Workshop on Machine Learning for Information Extraction, Orlando, Florida.
Peng, F. (1999). “Models Development in IE Tasks - A survey”. CS685 (Intelligent Computer Interface) course project, C omputer Science Department, University of Waterloo.
Stevenson M. & Greenwood M. A. (2006). “Comparing Information Extraction Pattern Models”, In Proceedings of the Workshop on Information Extraction Beyond The Document, Assoiation for Computational Linguistics, Sydney, pp.12-19.
Turno, J. Information Extraction, Multilinguality and Portability”. Revista Iberoamericana de Inteligencia Artificial, 2003,No. 22, pp. 57-78.
Basu,S., Mukherjee, A and Kilvansky, S.,(1996) Time series models for Internet traffic, Technical Report GIT-CC-95-27, Georgia Institure of Technology .
Leland, Will E., et al. "On the self-similar nature of Ethernet traffic." ACM SIGCOMM Computer Communication Review. Vol. 23. No. 4. ACM, 1993.
Gallant R. and Tauchen, G. (1989) Seminonparametric estimation of conditionally constrained heterogeneous processes: Asset pricing applications, Econometrica 57 1091–1120.
Carter R. and Crovella,M(1996). Dynamic server selection using bandwidth probing in wide-area networks, Technical Report TR-96-007, Boston University.
PAWLAK Z.Rough sets [J], Communications of ACM,1995, 38 (11) :89-95.