Point-Set Kernel Clustering
This addresses the trade-off between efficiency and effectiveness in clustering algorithms for large-scale datasets, offering a solution that improves both aspects.
The paper tackles the problem of clustering by introducing a new similarity measure called point-set kernel to compute similarity between an object and a set, resulting in a procedure that is both effective and efficient, running orders of magnitude faster on datasets with millions of points compared to state-of-the-art methods.
Measuring similarity between two objects is the core operation in existing clustering algorithms in grouping similar objects into clusters. This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects. The proposed clustering procedure utilizes this new measure to characterize every cluster grown from a seed object. We show that the new clustering procedure is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering algorithms are either efficient or effective. In comparison with the state-of-the-art density-peak clustering and scalable kernel k-means clustering, we show that the proposed algorithm is more effective and runs orders of magnitude faster when applying to datasets of millions of data points, on a commonly used computing machine.