Iterative Spectral Method for Alternative Clustering
This work addresses the need for efficient alternative clustering algorithms in data analysis, though it is incremental as it builds on KDAC.
The paper tackles the problem of finding an alternative clustering given an existing partition, proposing an Iterative Spectral Method (ISM) that improves the scalability of the state-of-the-art Kernel Dimension Alternative Clustering (KDAC) by up to 5 orders of magnitude in computation time.
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.