An Algorithm for Finding the Reduct and Core of the Consistent Dataset

In the large voluminous data it is very difficult to find useful patterns hidden in the data. Traditionally, the statistical methods are used to analyze the data for finding useful patterns. Another approach for finding the secreted information from the dataset is data mining. Data Mining, which is also known as KDD (Knowledge Discovery in Database), means a process of nontrivial extraction of implicit, previously unknown and potentially useful information and this information may be in the form of knowledge rules, constraints, and regularities from data in databases. Rough set theory provides a mathematical basis for Data Mining. This theory is used as a tool for extracting useful patterns hidden in data in the form of knowledge or rules. Knowledge reduction which includes attribute reduction and value reduction is an important topic in rough set literature. The attribute reduction is one of key processes for knowledge acquisition. In this research paper a new algorithm is presented for finding the reduct (reduced set of necessary attributes) and core (most important attribute of the dataset) of the dataset simultaneously and to solve the attribute reduction problem in RST. It is the most promising technique in the RST, a new mathematical approach to generate reduct and core. By using the proposed algorithm the redundant attributes are eliminated in order to generate the effective reduct set or to construct the core of the attribute set. The time and space complexity of the proposed algorithm is computed and is compared with other existing algorithms. It is established that the proposed algorithm decreases computation time and space while maintaining the quality and effectiveness of the results.