BLOCK-DBSCAN: Fast clustering for large scale data

Chen, Yewang; Zhou, Lida; Bouguila, Nizar; Wang, Cheng; Chen, Yi; Du, Ji-Xiang

doi:10.1016/j.patcog.2020.107624

Cited by 125 publications

(34 citation statements)

References 25 publications

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“…The density-reachable CBs were then merged, whereas NCBs were allocated to an appropriate cluster. They also proposed a grid-based clustering method named BLOCK-DBSCAN in two versions (L ∞ and L 2 ) for highdimensional big data in 2020 [26]. Using a computation reduction method, this idea proposes two techniques.…”

Section: Clustering Methods Based On a Single Machinementioning

confidence: 99%

K-DBSCAN: An improved DBSCAN algorithm for big data

2020

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Big data storage and processing are among the most important challenges now. Among data mining algorithms, DBSCAN is a common clustering method. One of the most important drawbacks of this algorithm is its low execution speed. This study aims to accelerate the DBSCAN execution speed so that the algorithm can respond to big datasets in an acceptable period of time. To overcome the problem, an initial grouping was applied to the data in this article through the K-means++ algorithm. DBSCAN was then employed to perform clustering in each group separately. As a result, the computational burden of DBSCAN execution reduced and the clustering execution speed increased significantly. Finally, border clusters were merged if necessary. According to the results of executing the proposed algorithm, it managed to greatly reduce the DBSCAN execution time (98% in the best-case scenario) with no significant changes in the qualitative evaluation criteria for clustering.

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Section: Clustering Methods Based On a Single Machinementioning

confidence: 99%

K-DBSCAN: An improved DBSCAN algorithm for big data

2020

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“…DBSCAN++ [74] O(mn), m: number of sampled objects Reduces the frequency of regional query operation by defining a chosen subset of objects to run on. Selects the subsets of an object by using the greedy initialization method and approximates k-center objects BLOCK-DBSCAN [75] Average complexity of O(n)…”

Section: Fast-dbscan [73]mentioning

confidence: 99%

Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms

Bushra

2021

IEEE Access

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The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering algorithm of the density-based clustering technique. It provides the ability to handle outlier objects, detect clusters of different shapes, and disregard the need for prior knowledge about existing clusters in a dataset. These features along with its simplistic approach helped it become widely applicable in many areas of science. However, for all its accolades, the DBSCAN still has limitations in terms of performance, its ability to detect clusters of varying densities, and its dependence on user input parameters. Multiple DBSCAN-inspired algorithms have been subsequently proposed to alleviate these and more problems of the algorithm. In this paper, the implementation, features, strengths, and drawbacks of the DBSCAN are thoroughly examined. The successive algorithms proposed to provide improvement on the original DBSCAN are classified based on their motivations and are discussed. Experimental tests were conducted to understand and compare the changes presented by a C++ implementation of these algorithms compared with the original DBSCAN algorithm. Finally, the analytical evaluation presented based on the results found.

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“…For example, SLIC shows advantages in generating sub-images that have good boundary compliance [20,21]. And, density based spatial clustering of application with noise (DBSCAN) [40,41] perform well in grouping sub-images which belongs to the same clusters.…”

Section: Introductionmentioning

confidence: 99%

Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network

2021

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Video flame and smoke-based fire detection usually exhibit large variations in the feature of color, texture, shapes, etc., caused by the complex environment. It is difficult to develop a robust method to detect fire based on single or multiple fire features. Since convolutional neural network (CNN) has reported state-of-the-art performance in a wide range of fields. This study present a method based on SLIC-DBSCAN and convolutional neural network to recognize flame and smoke modes connected to fire stages. First, simple linear iterative clustering (SLIC) is acted as the pre-processing step to over segment images into super-pixels. Then the use of density based spatial clustering of application with noise (DBSCAN) gathered the similar super-pixels into several clusters, which in turn provide better smoke detection accuracy by using CNN. Comparison studies are performed to base on smoke image from publicly available data and self-collected data. The experimental results demonstrated the improved smoke detection capabilities by the present method.

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BLOCK-DBSCAN: Fast clustering for large scale data

Cited by 125 publications

References 25 publications

K-DBSCAN: An improved DBSCAN algorithm for big data

K-DBSCAN: An improved DBSCAN algorithm for big data

Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering Algorithms

Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network

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