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Iterative Spectral Method for Alternative Clustering

Authors :
Wu, Chieh
Ioannidis, Stratis
Sznaier, Mario
Li, Xiangyu
Kaeli, David
Dy, Jennifer G.
Publication Year :
2019

Abstract

Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1909.03441
Document Type :
Working Paper