Database Based Validation of Union of Two Multigranular Rough Sets
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - November Issue 2013 by IJCTT Journal | ||
| Volume-5 Issue-5 | ||
| Year of Publication : 2013 | ||
| Authors :R. Raghavan |
R. Raghavan"IDatabase Based Validation of Union of Two Multigranular Rough Sets"International Journal of Computer Trends and Technology (IJCTT),V5(5):236-242 November Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- Most of the traditional tools for undertaking modeling, reasoning and other computing are found not only crisp but also highly deterministic and more precise in character which usually limits their applicability in real life situations led to the extension of the concept of crisp sets so as to model imprecise data and hence enhance their modeling capacity. One such method used to capture impreciseness was carried out by Pawlak who introduced the idea of rough sets, which is found to be an excellent tool to capture impreciseness in data. Several extensions have been made in different directions in order to improve the modeling capacity of the basic rough sets. One among such extension is rough set model based on multigranulations. Several fundamental properties of these types of rough sets have been studied . Pawlak introduced the types of rough sets in as an interesting characterization of rough sets by employing the ideas of lower and upper approximations of rough sets. There are two different ways of characterizing rough sets; the accuracy coefficient and the topological characterization introduced through the notion of types. As referred by Pawlak himself, in general rough sets by knowing the accuracy of a set, we were still unable to tell exactly its topological structure and also the present knowledge about the topological structure of the set gives no information about its accuracy. Therefore in practical applications of rough sets we combine both kinds of information about the borderline region, that is of the accuracy measure as well as the information about the topological classification of the set under consideration. Keeping this in mind, Tripathy and Mitra [9] have studied the types of rough sets by finding out the types of union and intersection of rough sets of different types. Later Raghavan et al have extended these results to the multigranular context in [12]. In this work I provided the database based validated results for the carried out results.
References
-
[1] Pawlak, Z.: Rough Sets, International Journal of Information and Computer Science, (1982), pp.341-346.
[2] Grzymala Busse, J.: Knowledge acquisition under uncertainty- a rough set approach, Journal of Intelligent and Robotics systems, 1, (1988), pp. 3 -1 6.
[3] Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, (1991).
[4] Tripathy, B.K.: On Approximation of classifications, rough equalities and rough equivalences, Studies in Computational Intelligence, vol.174, Rough Set Theory: A True Landmark in Data Analysis, Springer Verlag, (2009), pp.85 - 136.
[5] Tripathy, B.K., Ojha, J., Mohanty, D. and Prakash Kumar, Ch. M.S: On rough definability and types of approximation of classifications, vol.35, no.3, (2010), pp.197-215.
[6] Chan, C.C. and Grzymala Busse, J.: Rough Set boundaries as a tool for learning rules from examples, In: Proceedings of the ISMIS-89, 4th Int. Symposium on Methodologies for intelligent Systems, (1989), pp.281- 288.
[7] Chan, C.C. and Grzymala Busse, J.: On the attribute redundancy and the learning programs ID3, PRISM and LEM2, Department of Computer science, University of Kansas, TR-91-14, 20 December, (1991).
[8] Pawlak, Z.: Rough Classifications, international Journal of Man Machine Studies, 20, (1983), pp.469 – 483.
[9] Tripathy, B.K. and Mitra, A.: Topological properties of rough sets and their applications, International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), (Switzerland),vol.1, no.4, (2010),pp.355-369
[10] Qian, Y.H and Liang, J.Y.: Rough set method based on Multi-granulations, Proceedings of the 5th IEEE Conference on Cognitive Informatics, vol.1, (2006),pp.297 – 304.
[11] Qian, Y.H., Liang, J.Y and Dang, C.Y.: Pessimistic rough decision, in: Proceedings of RST 2010, Zhoushan, China, (2010), pp. 440-449.
[12] Raghavan, R and Tripathy, B.K. .: On Some Topological Properties of Multigranular Rough Sets, Journal of Advances in Applied science Research, Vol.2, no.3, (2011), pp.536-543.
[13] Tripathy, B.K. and Nagaraju, M.: On Some Topological Properties of Pessimistic Multigranular Rough Sets, International Journal of Intelligent Systems and Applications, Vol.4, No.8, (2012), pp.10-17.
[14] Raghavan, R and Tripathy, B.K.: On Some Comparison Properties of Rough Sets Based on Multigranulations and Types of Multigranular Approximations of Classifications, International Journal of Intelligent Systems and Applications, Volume 6, 2013, pp 70-77.
[15] Tripathy, B.K. and Raghavan, R.: Some Algebraic properties of Multigranulations and an Analysis of Multigranular Approximations of Classifications, International Journal of Information Technology and Computer Science, Volume 7, 2013, pp. 63-70.
Keywords :— Rough sets, multigranular rough sets ,database , topological property.