D-IMPACT: A Data Preprocessing Algorithm to Improve the Performance of Clustering
Tran Anh Vu, Osamu Hirose, Thammakorn Saethang, Lan Anh Nguyen, Xuan Tho Dang, Tu Kien T. Le, Duc Luu Ngo, Gavrilov Sergey, Mamoru Kubo, Yoichi M. A. Yamada, Kenji Satou · Journal of Software Engineering and Applications · 2014
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.