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dc.contributor.author Olukanmi, PO
dc.contributor.author Nelwamondo, Fulufhelo V
dc.contributor.author Marwala, T
dc.date.accessioned 2019-09-25T06:44:03Z
dc.date.available 2019-09-25T06:44:03Z
dc.date.issued 2019-01
dc.identifier.citation Olukanmi, 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. en_US
dc.identifier.isbn 978-1-7281-0369-3
dc.identifier.uri https://ieeexplore.ieee.org/abstract/document/8704854
dc.identifier.uri DOI: 10.1109/RoboMech.2019.8704854
dc.identifier.uri http://hdl.handle.net/10204/11121
dc.description Copyright: 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. en_US
dc.description.abstract 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 en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;22602
dc.subject Clustering LARge Applications en_US
dc.subject CLARA en_US
dc.subject K-means en_US
dc.subject K-medoids en_US
dc.subject Large datasets en_US
dc.subject Partitioning Around Medoids en_US
dc.subject PAM en_US
dc.subject PAM-lite en_US
dc.title Performance evaluation of sampling-based large-scale clustering algorithms en_US
dc.type Presentation en_US


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