4 accepted papers
We propose an accelerated exact $k$-means algorithm, Angle $k$-means. As its name suggests, the algorithm mainly leverages angular relationships between data points and cluster centers to reduce computational overhead. Although grounded in straightforward geometric principles, it delivers substant…
Most spectral clustering algorithms obtain firstly the embedding based on the similarity matrix of the input data and then discretize the embedding to get the final clustering result. So, their performance is greatly affected by the noise of the similarity matrix, and the two-step strategy may lead…
Spectral clustering and $k$-means, both as two major traditional clustering methods, are still attracting a lot of attention, although a variety of novel clustering algorithms have been proposed in recent years. Firstly, a unified framework of $k$-means and ratio-cut is revisited, and a novel and e…
How to effectively cluster large-scale image data sets is a challenge and is receiving more and more attention. To address this problem, a novel clustering method called fast clustering with co-clustering via discrete non-negative matrix factorization, is proposed. Inspired by co-clustering, our alg…