Version 2 2024-06-05, 09:38Version 2 2024-06-05, 09:38
Version 1 2022-02-10, 08:31Version 1 2022-02-10, 08:31
journal contribution
posted on 2024-06-05, 09:38authored byKM Ting, JR Wells, Ye ZhuYe Zhu
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.
History
Journal
IEEE Transactions on Knowledge and Data Engineering
Pagination
1-12
Location
Piscataway, N.J.
ISSN
1041-4347
eISSN
1558-2191
Language
eng
Publication classification
C1 Refereed article in a scholarly journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)