MulticlusterKDE: a new algorithm for clustering based on multivariate kernel density estimation
D Scaldelai1, L C Matioli2, S R Santos1
1Colegiado de Matemática, Universidade Estadual do Paraná - Unespar, Campo Mourão, Brazil.
Journal of Applied Statistics
|June 16, 2022
Summary
We introduce MulticlusterKDE, a novel algorithm for classifying database elements by similarity. This density-based clustering method is simple, efficient, and competitive with existing algorithms.
Area of Science:
- Computer Science
- Data Mining
- Machine Learning
Background:
- Database classification relies on identifying similarities between elements.
- Existing clustering algorithms have limitations in terms of complexity or parameter sensitivity.
Purpose of the Study:
- To propose and evaluate the MulticlusterKDE algorithm for database classification.
- To demonstrate the algorithm's simplicity, efficiency, and competitive performance.
Main Methods:
- Developed the MulticlusterKDE algorithm, optimizing a kernel density estimator with a multivariate Gaussian kernel.
- Implemented the algorithm in R software for practical application.
- Compared MulticlusterKDE against K-means, K-medoids, CLARA, DBSCAN, and PdfCluster.
Main Results:
- MulticlusterKDE successfully classifies database elements based on similarity.
- The number of clusters can be an optional input parameter.
- The algorithm is simple, always converges, and is computationally efficient.
Conclusions:
- MulticlusterKDE is a competitive and promising density-based clustering algorithm.
- Its features support further development and improvement in clustering techniques.
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