International Journal of Computer Theory and Engineering

Abstract

In the present day scenario, there are large volumes of data available in several fields, which we can make use of effectively, for decision making. This can be achieved by inducing rules through various rule induction approaches that are available. In this paper, we proposed a rule induction algorithm, ELEM, which is an enhanced version of one of the existing rule induction algorithms, LEM1 [3]. This is made effective by reducing the database scans required to generate the rules. Also, it provides an incremental approach which makes use of ELEM and deals with any kind of data changes in a dynamic information system. The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. In this paper, an incremental rule-extraction algorithm is proposed to resolve therefore mentioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database.