Summary
A group of fuzzy IF-THEN rules is belonging to one of the most popular, most effective, and user- friendliest knowledge representations. For this reason, extraction of these rules is becoming a more-and-more important part of the Data Mining stage in the Knowledge Discovery in Databases Process. In this paper, a direct algorithm for extracting fuzzy IF-THEN rules on the basis of linguistic variable elimination is described. The algorithm is implemented within a designed object-oriented software library Fuzzy Rule Miner. Besides the introduced algorithm, it implements two algorithms for fuzzy rule extraction based on using fuzzy decision trees of ID3 kind. An essential precondition for comparing the implemented algorithms and for verifying the legitimacy of the introduced algorithm is performance of experiments. The goal of experiments is to take in the behavior of algorithms on testing databases from the UCI Repository of Machine Learning Databases and to make comparisons of algorithms with one another. According to the conducted experiments, the introduced algorithm achieves high accuracy levels of discovered knowledge. The paper also contains a classification of rules and a specification of the Fuzzy Rule Discovery in Databases Process.
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Discovering Fuzzy Rules in Databases with Linguistic Variable Elimination
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1. IntroductionAt present, people have to manage more and more data of diverse kind. Large amount of existing data causes a lot of problems of various kind. One of the most visible of them is a person's or human team's inability to be able to maintain and process data in reasonable time. Because information technologies are used on a mass scale, large amount of other data arises. This kind of data can provide the runners or the owners of these systems useful information. As a result of the exponential growth of commercially utilizable data, database systems are incessantly developed. Database systems are a tried tool for manipulation with large data files, i.e. with large databases. Nowadays, most frequently used database systems are relational database systems. They keep databases in a form of relational tables and allow us to work with them. The columns of these tables are interpreted as attributes and their rows are interpreted as instances. There are basically two types of attributes: categorical (discreet) attributes and numerical (continuous) attributes. Categorical values are defined for categorical attributes and numerical values are defined for numerical attributes. An example of a categorical value is uhof and an example of a numerical value is "25" . When dependences are searched in a table with attributes and instances, a reliable tool for their modeling and analysis is required. Statistics is this kind of tool. Statistics and databas...See the full content of this document
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