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Rough Sets Theory as Symbolic Data Mining Method: An Application on Complete Decision Table |
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PP: 35-47 |
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Author(s) |
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Mert BAL,
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Abstract |
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| Data mining and usage of the useful patterns that reside in the databases have become a very important research area because of the rapid developments in both computer hardware and software industries. In parallel with the rapid increase in the data stored in the databases, effective use of the data is becoming a problem. To discover the rules or interesting and useful patterns from these stored data, data mining techniques are used. If data is incomplete or inaccurate, the results extracted from the database during the data discovery phase would be inconsistent and meaningless. Rough sets theory is a new mathematical approach used in the intelligent data analysis and data mining if data is uncertain or incomplete. This approach is of great importance in cognitive science and artificial intelligence, especially in machine learning, decision analysis, expert systems and inductive reasoning.
There are many advantages of rough set approach in intelligent data analysis. Some of these advantages are being suitable for parallel processing, finding minimal data sets, supplying effective algorithms to discover hidden patterns in data, valuation of the meaningfulness of the data, producing decision rule set from data, being easy to understand and the results obtained can be interpreted clearly. In the last years, rough sets theory is widely used in different areas like engineering, banking and finance.
In this study, the mathematical principles of rough sets theory are explained and a sample application about rule discovery from a decision table by using different algorithms in rough sets theory is presented.
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