Olukanmi, PONelwamondo, Fulufhelo VMarwala, T2019-09-252019-09-252019-01Olukanmi, P.O., Nelwamondo, F.V. and Marwala, T. 2019. PAM-lite: Fast and accurate k-medoids clustering for massive datasets. SAUPEC/RobMech/PRASA Conference, Bloemfontein, South Africa, 28-30 January 2019, pp 200-204.978-1-7281-0369-3https://ieeexplore.ieee.org/document/8704767DOI: 10.1109/RoboMech.2019.8704767http://hdl.handle.net/10204/11120Copyright: 2019 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website.The Partitioning Around Medoids (PAM) clustering algorithm is well-known for its robustness and accuracy, but it is computationally expensive. This paper proposes a fast and accurate version, named PAM-lite. Like CLARA which also addresses PAM's inefficiency, PAM-lite applies PAM to random samples. However, unlike CLARA, it does not choose one of the obtained medoid sets (which would involve evaluating each set), but simply applies PAM again to the combination of all the obtained medoids. This simple change yields accuracy and speed improvement. We discuss the rationale behind PAM-lite's approach and evaluate the algorithm on benchmark datasets. In all cases tested, PAM-lite achieves better speed-up and clustering quality than CLARA; the speed-up margin increasing with problem size. PAM-lite competes so closely with the clustering quality produced by the full PAM algorithm, that in one high cluster variance case, it beats PAM's clustering quality slightly.enClustering LARge ApplicationsCLARAK-medoidsPartitioning Around MedoidsPAMConference PresentationOlukanmi, P., Nelwamondo, F. V., & Marwala, T. (2019). PAM-lite: Fast and accurate k-medoids clustering for massive datasets. IEEE. http://hdl.handle.net/10204/11120Olukanmi, PO, Fulufhelo V Nelwamondo, and T Marwala. "PAM-lite: Fast and accurate k-medoids clustering for massive datasets." (2019): http://hdl.handle.net/10204/11120Olukanmi P, Nelwamondo FV, Marwala T, PAM-lite: Fast and accurate k-medoids clustering for massive datasets; IEEE; 2019. http://hdl.handle.net/10204/11120 .TY - Conference Presentation AU - Olukanmi, PO AU - Nelwamondo, Fulufhelo V AU - Marwala, T AB - The Partitioning Around Medoids (PAM) clustering algorithm is well-known for its robustness and accuracy, but it is computationally expensive. This paper proposes a fast and accurate version, named PAM-lite. Like CLARA which also addresses PAM's inefficiency, PAM-lite applies PAM to random samples. However, unlike CLARA, it does not choose one of the obtained medoid sets (which would involve evaluating each set), but simply applies PAM again to the combination of all the obtained medoids. This simple change yields accuracy and speed improvement. We discuss the rationale behind PAM-lite's approach and evaluate the algorithm on benchmark datasets. In all cases tested, PAM-lite achieves better speed-up and clustering quality than CLARA; the speed-up margin increasing with problem size. PAM-lite competes so closely with the clustering quality produced by the full PAM algorithm, that in one high cluster variance case, it beats PAM's clustering quality slightly. DA - 2019-01 DB - ResearchSpace DP - CSIR KW - Clustering LARge Applications KW - CLARA KW - K-medoids KW - Partitioning Around Medoids KW - PAM LK - https://researchspace.csir.co.za PY - 2019 SM - 978-1-7281-0369-3 T1 - PAM-lite: Fast and accurate k-medoids clustering for massive datasets TI - PAM-lite: Fast and accurate k-medoids clustering for massive datasets UR - http://hdl.handle.net/10204/11120 ER -