SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets

Negar Ohadi, A. Kamandi, M. Shabankhah, Seyed Mohsen Fatemi, S. Hosseini, Alireza Mahmoudi
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引用次数: 16

Abstract

Data clustering aims to discover the underlying structure of data. it has many applications in data analysis and it is one of the most widely used tools in data mining. DBSCAN is one of the most famous clustering algorithms. its advantages are to identify clusters of various shapes and define the number of clusters. Since DBSCAN is sensitive to its parameters which are ε and MinPts, it may perform poorly when the dataset is unbalanced. To solve this problem, this paper proposes a sliding window DBSCAN clustering algorithm that uses Gridding and local parameters for unbalanced data which we will refer to as SW-DBSCAN. The algorithm divides the dataset into several grids. The size and shape of each gird depends on the specimen density specification. Then, for each grid, the parameters are adjusted for local clustering and eventually merging data zones. Experimental results show that this algorithm can help to improve the performance of the DBSCAN algorithm and can deal with arbitrary data and asymmetric data.
SW-DBSCAN:基于网格的大数据集DBSCAN算法
数据聚类的目的是发现数据的底层结构。它在数据分析中有许多应用,是数据挖掘中使用最广泛的工具之一。DBSCAN是最著名的聚类算法之一。它的优点是可以识别各种形状的簇,并定义簇的数量。由于DBSCAN对其参数ε和MinPts很敏感,当数据集不平衡时,它的性能可能会很差。为了解决这个问题,本文提出了一种滑动窗口DBSCAN聚类算法,该算法对不平衡数据使用网格和局部参数,我们将其称为SW-DBSCAN。该算法将数据集划分为多个网格。每个网格的大小和形状取决于试样密度规格。然后,对于每个网格,调整参数以进行局部聚类并最终合并数据区域。实验结果表明,该算法可以提高DBSCAN算法的性能,并能处理任意数据和非对称数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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