Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms-Reference-Cited by-同舟云学术

Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms

Published:2021-11 Issue: Volume:101 Page:101804
ISSN:0306-4379
Container-title:Information Systems
language:en
Short-container-title:Information Systems

Author:

Schubert Erich^ORCID,Rousseeuw Peter J.^ORCID

Funder

Deutsche Forschungsgemeinschaft

KU Leuven

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference61 articles.

1. On some clustering techniques;Bonner;IBM J. Res. Dev.,1964

2. Why so many clustering algorithms: a position paper;Estivill-Castro;SIGKDD Explor.,2002

3. Clustering by means of medoids;Kaufman,1987

4. Partitioning around medoids (program PAM);Kaufman,1990

5. M. Ester, H. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining (KDD-96), 1996, pp. 226–231.

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