Medoid-based clustering using ant colony optimization
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- 作者：Héctor D. Menéndez ; Fernando E. B. Otero ; David Camacho
- 刊名：Swarm Intelligence
- 出版年：2016
- 出版时间：June 2016
- 年：2016
- 卷：10
- 期：2
- 页码：123-145
- 全文大小：841 KB
- 刊物类别：Engineering
- 刊物主题：Communications Engineering and Networks<br>Computer Communication Networks<br>Probability and Statistics in Computer Science<br>Computer Systems Organization and Communication Networks<br>Coding and Information Theory<br>Electronic and Computer Engineering<br>
- 出版者：Springer New York
- ISSN：1935-3820
- 卷排序：10
文摘
The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets.KeywordsAnt colony optimizationClusteringData mining Machine learningMedoidAdaptive