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 |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Olukanmi, P., Nelwamondo, F. V., & Marwala, T. (2019). Performance evaluation of sampling-based large-scale clustering algorithms. IEEE. http://hdl.handle.net/10204/11121 |
en_ZA |
dc.identifier.chicagocitation |
Olukanmi, PO, Fulufhelo V Nelwamondo, and T Marwala. "Performance evaluation of sampling-based large-scale clustering algorithms." (2019): http://hdl.handle.net/10204/11121 |
en_ZA |
dc.identifier.vancouvercitation |
Olukanmi P, Nelwamondo FV, Marwala T, Performance evaluation of sampling-based large-scale clustering algorithms; IEEE; 2019. http://hdl.handle.net/10204/11121 . |
en_ZA |
dc.identifier.ris |
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 -
|
en_ZA |