Attribute reduction of an information system is a key problem in rough set theory and its applications. Rough set theory has been one of the most successful methods used for feature selection. Rough set is one of the most useful data mining techniques. This paper proposes relative reduct to solve the attribute reduction problem in roughest theory. It is the most promising technique in the Rough set theory, a new mathematical approach to reduct car dataset using relative reduct algorithm. The redundant attributes are eliminated in order to generate the effective reduct set (i.e., reduced set of necessary attributes) or to construct the core of the attribute set. The technique was originally proposed to avoid the calculation of discernibility functions or positive regions, which can be computationally expensive without optimizations. This paper analyses the efficiency of the proposed backward relative reduct algorithm against forward selection algorithm. The experiments are carried out on car data base of UCI machine learning repository.

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The successful application of data mining in fields like ebusiness, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Data is a great asset to meet long-term goals of any organization and can help to improve customer relationship management. It can also benefit healthcare providers like hospitals, clinics and physicians, and patients, for example, by identifying effective treatments and best practices popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Efficient clustering tools reduce demand on costly healthcare resources. It can help physicians cope with the information overload and can assist in future planning for improved services. Clustering results are used to study independence or correlation between diseases and for better insight into medical survey data. To achieve this, create clustering algorithms that enhances the traditional K-Means, DB-Scan and Fuzzy C-Means algorithms.

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