Scalable Rough-fuzzy Weighted Leader based Non-parametric Methods for Large Data Sets.

Abstract: Popular non-parametric methods like k-nearest neighbor classifier and density based clustering method like DBSCAN show good performance when data set sizes are large. The time complexity to find a density at a point in the data set is O(n) where n is the size of the data set, hence these non-parametric methods are not scalable for large data sets. A two level rough fuzzy weighted leader based classifier has been developed which is a scalable and efficient method for classification. However, a generalized model does not exist to estimate density non-parametrically that can be used for density based classification and clustering. This paper presents a generalized model which proposes a single level rough fuzzy weighted leader clustering method to condense data set inorder to reduce computational burden and use these rough-fuzzy weighted leaders to estimate density at a point in the data set for classification and clustering. We show that the proposed rough fuzzy weighted leader based non-parametric methods are fast and efficient when compared with related existing methods interms of accuracy and computational time. [Copyright &y& Elsevier]