首页|Parallel complete gradient clustering algorithm and its properties
Parallel complete gradient clustering algorithm and its properties
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NSTL
Elsevier
Clustering is one of the most important tasks in the field known as 'Exploratory Data Analysis' (EDA). It explores the dependencies hidden in individual data attributes, dividing them from one set into smaller subsets. In this paper, a Parallel Complete Gradient Clustering Algorithm (PCGCA) is proposed. The Complete Gradient Clustering Algorithm (CGCA) provides a natural interpretation combined with no need for assumptions regarding the number of clusters, making it an appealing choice. Moreover, in CGCA, internal optimization procedures point out the parameters influencing the size of clusters. Algorithms based on kernel density estimation can, therefore, be applied for diverse practical scenarios. Another very useful usage is outlier detection - which is especially important in the currently fast-growing data industry. The described algorithm has been validated in terms of both the speed of calculation and the quality of the obtained solution. The quality of the solution was evaluated with the use of eleven clustering indexes calculated on six data sets. In addition, the obtained result was compared with several classical well-known methods of clustering.(c) 2022 Elsevier Inc. All rights reserved.
Data scienceExploratory data analysisClusteringClustering indexesDensity clusteringParallelization
NSTL
Elsevier
