A weight-incorporated similarity-based clustering ensemble method - ShiYao Liu; Qi Kang; Jing An; MengChu Zhou (Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, 2014)
Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.