期刊
JOURNAL OF APPLIED STATISTICS
卷 49, 期 1, 页码 98-121出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02664763.2020.1799958
关键词
Kernel density estimation; Gaussian kernel; clustering data; optimization method; multiclusterKDE
资金
- CAPES, Brazil
- CNPq, Brazil
In this paper, the proposed MulticlusterKDE algorithm is used to classify elements of a database based on their similarity. One of the main features of this algorithm is the optional input parameter for the number of clusters. The algorithm is simple, well defined, converges in a finite number of steps, and shows competitive performance compared to other algorithms.
In this paper, we propose the MulticlusterKDE algorithm applied to classify elements of a database into categories based on their similarity. MulticlusterKDE is centered on the multiple optimization of the kernel density estimator function with multivariate Gaussian kernel. One of the main features of the proposed algorithm is that the number of clusters is an optional input parameter. Furthermore, it is very simple, easy to implement, well defined and stops at a finite number of steps and it always converges regardless of the data set. We illustrate our findings by implementing the algorithm in R software. The results indicate that the MulticlusterKDE algorithm is competitive when compared to K-means, K-medoids, CLARA, DBSCAN and PdfCluster algorithms. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field that can be used as basis for its improvement and also for the development of new density-based clustering algorithms.
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