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Friday, May 14, 2010

Rough Set Theory : Brief Notes

Wikipedia says:
A rough set, first described by Zdzisław I. Pawlak, is a formal approximation of a crisp set (i.e., conventional set) in terms of a pair of sets which give the lower and the upper approximation of the original set. In the standard version of rough set theory (Pawlak 1991), the lower- and upper-approximation sets are crisp sets, but in other variations, the approximating sets may be fuzzy sets.

I am providing you with some info on where this theory can be applied. For more details and fundamentals of rough set theory wait for my posts in near future.

Rough set methods can be applied as a component of hybrid solutions in machine learning and data mining. They have been found to be particularly useful for rule induction and feature selection (semantics-preserving dimensionality reduction). Rough set-based data analysis methods have been successfully applied in bioinformatics, economics and finance, medicine, multimedia, web and text mining, signal and image processing, software engineering, robotics, and engineering (e.g. power systems and control engineering). Recently, rough set principles have been also used in the open source database software by Infobright.
Applications of rough sets theory to knowledge discovery involve collecting empirical data and building classification models from the data [1–3]. The main distinction in this approach is it's primarily concerned with the acquisition of decision tables from data followed by their analysis and simplification by identifying attribute dependencies, minimal nonredundant subsets of attributes, most important attributes, and minimized rules. The technology of rough sets has been applied to practical knowledge discovery problems since late 1980s. The first commercial software tool for rough set-based knowledge discovery application development was sold in the early 1990s by Reduct Systems, Inc. [4]. It was called Datalogic. Here, I briefly discuss some representative applications of this technology using Datalogic and other tools (See [5, 6] for more indepth details). Most of these apps fall into categories such as market research, medicine, control, drug and new material design research, stock market, pattern recognition, and environmental engineering.

This is awesome theory that can be used in making spam less meta search egine also. The anti-spamming nature can be exploited to make spam-less mail systems. :)

Keep waiting for more info;) (or write to me)

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