A new hybrid method based on partitioning-based DBSCAN and ant clustering

Jiang, Hua; Li, Jing; Yi, Shenghe; Wang, Xiangyang; Hu, Xin

doi:10.1016/j.eswa.2011.01.135

Cited by 85 publications

(46 citation statements)

References 16 publications

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“…It is capable of finding clusters of any shape as well as identifying and filtering noise points. The clustering results are insensitive to the input order of the dataset [13,14]. The algorithm can automatically determine the number of clusters without prior knowledge, and the number of clusters is equal to the number of source signals.…”

Section: Mixing Matrix Estimation Based On Dbscanmentioning

confidence: 99%

“…However, the K-means algorithm is sensitive to the original cluster center and the number of sources need to be determined first [9][10][11]. Density-based spatial clustering of applications with noise (DBSCAN) [12][13][14] can overcome the drawbacks of the K-means clustering algorithm, but the parameter selection involved in this method is a challenging task. Further, single source point detection [15][16][17] has been proposed to address the problem of low signal sparsity [18][19][20][21], but it suffers from issues such as high complexity and conditions that are difficult to satisfy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel mixing matrix estimation approach in underdetermined blind source separation

Sun

Wen

et al. 2016

Neurocomputing

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Section: Mixing Matrix Estimation Based On Dbscanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel mixing matrix estimation approach in underdetermined blind source separation

Sun

Wen

et al. 2016

Neurocomputing

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“…Jiang et al (2010) have developed new hybrid clustering algorithms by combining the ACO with kharmonic means algorithm. A partitioned-based DBSCAN algorithm is proposed for clustering by combining the ant clustering algorithm (Jiang et al 2011). …”

Section: Ant Colony-based Clusteringmentioning

confidence: 99%

Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy

Kumar

Sahoo

2015

Soft Comput

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Clustering is a popular data analysis technique, which is applied for partitioning of datasets. The aim of clustering is to arrange the data items into clusters based on the values of their attributes. Magnetic charge system search (MCSS) algorithm is a new meta-heuristic optimization algorithm inspired by the electromagnetic theory. It has been proved better than other meta-heuristics. This paper presents a new hybrid meta-heuristic algorithm by combining both MCSS and particle swarm optimization (PSO) algorithms, which is called MCSS-PSO, for partitional clustering problem. Moreover, a neighborhood search strategy is also incorporated in this algorithm to generate more promising solutions. The performance of the proposed MCSS-PSO algorithm is tested on several benchmark datasets and its performance is compared with already existing clustering algorithms such as K-means, PSO, genetic algorithm, ant colony optimization, charge system search, chaotic charge system search algorithm, and some PSO variants. From the experimental results, it can be seen that performance of the proposed algorithm is better than the other algorithms being compared and it can be effectively used for partitional clustering problem.

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“…There have been many efforts to mitigate the drawbacks of DB-SCAN clustering algorithm. Jiang et al [12] present a new hybrid method based on partitioning-based DBSCAN and ant clustering to improve memory usage in DBSCAN. The GMDBSCAN algorithm [13] based on spatial index and grid technique has been proposed to improve clustering in multi-density data sets.…”

Section: Introductionmentioning

confidence: 99%

Choosing DBSCAN Parameters Automatically using Differential Evolution

Karami¹,

Johansson²

2014

IJCA

105

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Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DB-SCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.

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A new hybrid method based on partitioning-based DBSCAN and ant clustering

Cited by 85 publications

References 16 publications

Novel mixing matrix estimation approach in underdetermined blind source separation

Novel mixing matrix estimation approach in underdetermined blind source separation

Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy

Choosing DBSCAN Parameters Automatically using Differential Evolution

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