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 a...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-dimensional datasets and practical datasets show that this algorithm can produce new datasets such that the performance of the clustering algorithm is improved.展开更多
文摘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-dimensional datasets and practical datasets show that this algorithm can produce new datasets such that the performance of the clustering algorithm is improved.
