文章基本信息

标题：A Complexity Survey on Density based Spatial Clustering of Applications of Noise Clustering Algorithms
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作者：Boulchahoub Hassan ; Rachiq Zineb ; Labriji Amine 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：2
页码：664-670
DOI：10.14569/IJACSA.2021.0120283
出版社：Science and Information Society (SAI)
摘要：Data Clustering is an interesting field of unsupervised learning that has been extensively used and discussed over several research papers and scientific studies. It handles several issues related to data analysis by grouping similar entities into the same set. Up to now, many algorithms were developed for clustering using several techniques including centroids, density and dendrograms approaches. We count nowadays more than 100 diverse algorithms and many enhancements for each algorithm. Therefore, data scientists still struggle to find the best clustering method to use among this diversity of techniques. In this paper we present a survey on DBSCAN algorithm and its enhancements with respect to time requirement. A significant comparison of DBSCAN versions is also illustrated in this paper to help data scientist make decisions about the best version of DBSCAN to use.
关键词：Unsupervised learning; clustering; density clustering; DBSCAN