Ultra-Scalable Spectral Clustering and Ensemble Clustering

• Published in: IEEE transactions on knowledge and data engineering Vol. 32; no. 6; pp. 1212 - 1226
• Main Authors: Huang, Dong, Wang, Chang-Dong, Wu, Jian-Sheng, Lai, Jian-Huang, Kwoh, Chee-Keong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Bipartite graph; Clustering; Clustering algorithms; Complexity theory; Data clustering; Datasets; ensemble clustering; Graph theory; large-scale clustering; large-scale datasets; nonlinearly separable datasets; Robustness; Scalability; Sparse matrices; Spectra; spectral clustering

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representativ...