Two-Phase Spectral Clustering Based on Discretization
Qiju Kang, Ying Ping Qian, Lijuan Sun, Hai Bo Yu, Jianyu Wang · 2013
As spectral clustering has become increasingly popular in recent years, further research on it is very important. Due to the important role of discretization in data mining, a new clustering approach integrating discretization with density-sensitive spectral clustering namely density-sensitive spectral clustering of categorized data (DSSCCAT) is proposed. To alleviate the high computational complexity of DSSCCAT, two-phase spectral clustering (TPSC) algorithm is proposed, which involves two phases: construct the representatives of the original dataset and cluster the representatives with DSSCCAT. Experimental results on UCI datasets show the feasibility of combining discretization with density-sensitive spectral clustering. TPSC can obtain desirable clusters with high performance. Furthermore, TPSC outperforms DSSCCAT obviously in terms of computational time.