TITLE:
D-IMPACT: A Data Preprocessing Algorithm to Improve the Performance of Clustering
AUTHORS:
Vu Anh Tran, Osamu Hirose, Thammakorn Saethang, Lan Anh T. Nguyen, Xuan Tho Dang, Tu Kien T. Le, Duc Luu Ngo, Gavrilov Sergey, Mamoru Kubo, Yoichi Yamada, Kenji Satou
KEYWORDS:
Attraction, Clustering, Data Preprocessing, Density, Shrinking
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.7 No.8,
July
8,
2014
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-dimensional datasets and practical
datasets show that this algorithm can produce new datasets such that the
performance of the clustering algorithm is improved.