D-IMPACT: A Data Preprocessing Algorithm to Improve the Performance of Clustering

Abstract

In this study, we propose a data preprocessing algorithm called D-IMPACT inspired by the IMPACT clustering algorithm. D-IMPACT iteratively moves data points based on attraction and density to detect and remove noise and outliers, and separate clusters. Our experimental results on two-di- mensional datasets and practical datasets show that this algorithm can produce new datasets such that the performance of the clustering algorithm is improved.