Olukanmi, PONelwamondo, Fulufhelo VMarwala, T2019-09-252019-09-252019-01Olukanmi, P.O., Nelwamondo, F.V. and Marwala, T. 2019. Performance evaluation of sampling-based large-scale clustering algorithms. SAUPEC/RobMech/PRASA Conference, Bloemfontein, South Africa, South Africa, 28-30 January 2019, pp 200-204.978-1-7281-0369-3https://ieeexplore.ieee.org/abstract/document/8704854DOI: 10.1109/RoboMech.2019.8704854http://hdl.handle.net/10204/11121Copyright: 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.Using benchmark datasets, we study the performances of three efficient clustering algorithms which find cluster centers using a fixed number of random samples. The algorithms are also compared with two other (well-known) algorithms, namely k-means and PAM. One of the efficient algorithms, CLARA, is well-known while the other two, k-means-lite and PAM-lite, were introduced recently. CLARA and PAM-lite are based on the k-medoids approach, while k-means-lite adopts the k-means approach. The study shows that k-means-lite is the most efficient, followed by PAM-lite which is faster than CLARA. PAM-lite exhibits the best balance of efficiency and accuracy; it produces the most competitive results relative to PAM which is the most accurate but most inefficientenClustering LARge ApplicationsCLARAK-meansK-medoidsLarge datasetsPartitioning Around MedoidsPAMPAM-liteConference PresentationOlukanmi, P., Nelwamondo, F. V., & Marwala, T. (2019). Performance evaluation of sampling-based large-scale clustering algorithms. IEEE. http://hdl.handle.net/10204/11121Olukanmi, PO, Fulufhelo V Nelwamondo, and T Marwala. "Performance evaluation of sampling-based large-scale clustering algorithms." (2019): http://hdl.handle.net/10204/11121Olukanmi P, Nelwamondo FV, Marwala T, Performance evaluation of sampling-based large-scale clustering algorithms; IEEE; 2019. http://hdl.handle.net/10204/11121 .TY - Conference Presentation AU - Olukanmi, PO AU - Nelwamondo, Fulufhelo V AU - Marwala, T AB - Using benchmark datasets, we study the performances of three efficient clustering algorithms which find cluster centers using a fixed number of random samples. The algorithms are also compared with two other (well-known) algorithms, namely k-means and PAM. One of the efficient algorithms, CLARA, is well-known while the other two, k-means-lite and PAM-lite, were introduced recently. CLARA and PAM-lite are based on the k-medoids approach, while k-means-lite adopts the k-means approach. The study shows that k-means-lite is the most efficient, followed by PAM-lite which is faster than CLARA. PAM-lite exhibits the best balance of efficiency and accuracy; it produces the most competitive results relative to PAM which is the most accurate but most inefficient DA - 2019-01 DB - ResearchSpace DP - CSIR KW - Clustering LARge Applications KW - CLARA KW - K-means KW - K-medoids KW - Large datasets KW - Partitioning Around Medoids KW - PAM KW - PAM-lite LK - https://researchspace.csir.co.za PY - 2019 SM - 978-1-7281-0369-3 T1 - Performance evaluation of sampling-based large-scale clustering algorithms TI - Performance evaluation of sampling-based large-scale clustering algorithms UR - http://hdl.handle.net/10204/11121 ER -