Evaluation of data mining strategies using fuzzy clustering in dynamic environment

Abstract

The recent applications of data mining such as biological, scientific, financial and others are changing data regularly, which is uncertain and incomplete. For finding tendency in these data up-to-date, we need to modify existing data mining algorithms with dynamic characteristics. Soft computing methods are suitable for finding changes in uncertain data. In order to adopt change in data we can apply any of two approaches, update algorithm by ignoring earlier state or update with respect to earlier state. In this paper, we have framed two fuzzy clustering methods based on these approaches and implementation done using R software with comparison.