DPISCAN: Distributed and parallel architecture with indexing for structural clustering of massive dynamic graphs

Kumar, Dhananjay; D’Mello, Demian Antony

doi:10.1007/s41060-021-00303-y

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Clustering algorithms can generally be used in partition-based [8], hierarchy-based [9], density-based [10], grid-based [11], model-based [12], and graph-based [13] ways to perform classification. The k-means algorithm is the most traditional method of partitioning clusters [14].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm

Wei

Wang

Han

et al. 2023

Entropy

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The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best Eps value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm’s performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.

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