Abstract
Clustering is an important task in machine learning and data mining. Due to various applications that use clustering, numerous clustering methods were proposed. One well-known, simple, and widely used clustering algorithm is k-means. The main problem of this algorithm is its tendency of getting trapped into local minimum because it does not have any kind of global search. Clustering is a hard optimization problem, and swarm intelligence stochastic optimization algorithms are proved to be successful for such tasks. In this paper, we propose recent swarm intelligence elephant herding optimization algorithm for data clustering. Local search of the elephant herding optimization algorithm was improved by k-means. The proposed method was tested on six benchmark datasets and compared to other methods from literature. Based on the obtained results it can be concluded that the proposed method finds better clusters when silhouette score is used as the quality measure.
| Original language | English |
|---|---|
| Title of host publication | Information and Communication Technology for Intelligent Systems - Proceedings of ICTIS 2018 |
| Editors | Amit Joshi, Suresh Chandra Satapathy |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 665-673 |
| Number of pages | 9 |
| ISBN (Print) | 9789811317460 |
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
| Event | 3rd International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018 - Ahmedabad, India Duration: 6 Apr 2018 → 7 Apr 2018 |
Publication series
| Name | Smart Innovation, Systems and Technologies |
|---|---|
| Volume | 107 |
| ISSN (Print) | 2190-3018 |
| ISSN (Electronic) | 2190-3026 |
Other
| Other | 3rd International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018 |
|---|---|
| Country | India |
| City | Ahmedabad |
| Period | 6/4/18 → 7/4/18 |
Fingerprint
Keywords
- Clustering
- Data mining
- Elephant herding optimization
- K-means
- Metaheuristics
- Swarm intelligence
ASJC Scopus subject areas
- Decision Sciences(all)
- Computer Science(all)
Cite this
Combined elephant herding optimization algorithm with k-means for data clustering. / Tuba, Eva; Dolicanin-Djekic, Diana; Jovanovic, Raka; Simian, Dana; Tuba, Milan.
Information and Communication Technology for Intelligent Systems - Proceedings of ICTIS 2018. ed. / Amit Joshi; Suresh Chandra Satapathy. Springer Science and Business Media Deutschland GmbH, 2019. p. 665-673 (Smart Innovation, Systems and Technologies; Vol. 107).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Combined elephant herding optimization algorithm with k-means for data clustering
AU - Tuba, Eva
AU - Dolicanin-Djekic, Diana
AU - Jovanovic, Raka
AU - Simian, Dana
AU - Tuba, Milan
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Clustering is an important task in machine learning and data mining. Due to various applications that use clustering, numerous clustering methods were proposed. One well-known, simple, and widely used clustering algorithm is k-means. The main problem of this algorithm is its tendency of getting trapped into local minimum because it does not have any kind of global search. Clustering is a hard optimization problem, and swarm intelligence stochastic optimization algorithms are proved to be successful for such tasks. In this paper, we propose recent swarm intelligence elephant herding optimization algorithm for data clustering. Local search of the elephant herding optimization algorithm was improved by k-means. The proposed method was tested on six benchmark datasets and compared to other methods from literature. Based on the obtained results it can be concluded that the proposed method finds better clusters when silhouette score is used as the quality measure.
AB - Clustering is an important task in machine learning and data mining. Due to various applications that use clustering, numerous clustering methods were proposed. One well-known, simple, and widely used clustering algorithm is k-means. The main problem of this algorithm is its tendency of getting trapped into local minimum because it does not have any kind of global search. Clustering is a hard optimization problem, and swarm intelligence stochastic optimization algorithms are proved to be successful for such tasks. In this paper, we propose recent swarm intelligence elephant herding optimization algorithm for data clustering. Local search of the elephant herding optimization algorithm was improved by k-means. The proposed method was tested on six benchmark datasets and compared to other methods from literature. Based on the obtained results it can be concluded that the proposed method finds better clusters when silhouette score is used as the quality measure.
KW - Clustering
KW - Data mining
KW - Elephant herding optimization
KW - K-means
KW - Metaheuristics
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85059075735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059075735&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-1747-7_65
DO - 10.1007/978-981-13-1747-7_65
M3 - Conference contribution
AN - SCOPUS:85059075735
SN - 9789811317460
T3 - Smart Innovation, Systems and Technologies
SP - 665
EP - 673
BT - Information and Communication Technology for Intelligent Systems - Proceedings of ICTIS 2018
A2 - Joshi, Amit
A2 - Satapathy, Suresh Chandra
PB - Springer Science and Business Media Deutschland GmbH
ER -