KR-DBSCAN: A density-based clustering algorithm based on reverse nearest neighbor and influence space

Hu, Lihua; Liu, Hongkai; Zhang, Jifu; Liu, Aiqin

doi:10.1016/j.eswa.2021.115763

Cited by 29 publications

(6 citation statements)

References 20 publications

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“…Other variants of DBSCAN do exist. For example, GDB-SCAN [54], KR-DBSCAN [55], and NS-DBSCAN [56]. All of these methods are proven to be adequate to cluster data points with differences in the density level.…”

Section: Density-based Clustering Methodsmentioning

confidence: 99%

Enhanced Approach for Agglomerative Clustering Using Topological Relations

et al. 2023

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Spatial data clustering has long been used to facilitate the knowledge discovery process. Several approaches have been proposed in the literature for detecting and understanding hidden patterns. These approaches are based on different perspectives and can be roughly categorized into several main categories, including centroid-based, density-based, grid-based, and hierarchy-based clustering. In spite of being a very mature research area, existing spatial clustering techniques usually depend on user parameters and continue to utilize distances between objects as their similarity measure. In turn, clustering approaches are generally suffering from performance and scalability issues. To address this problem, we propose ACUTE, an efficient and scalable approach that detects both synthetic and real-world spatial clusters. ACUTE ascertains both the intra-cluster compactness (similarities) and inter-cluster connectedness (dissimilarities) of spatial objects by assessing the topological relations of their corresponding spatial points. While conventional methods conduct clustering according to pairwise comparisons of the distances between objects, our approach focuses on leveraging topological relations that reduce the distances required to be calculated. This in turn minimizes the number of comparisons required, thus enhancing the efficiency and increasing the scalability. To evaluate the accuracy of ACUTE, it has been extensively tested against twelve (12) synthetic datasets and five (5) actual datasets including one location-based (network) dataset. Results show that ACUTE has great performance when compared with state-of-the-art clustering techniques in terms of several evaluation metrics, including precision, recall, and error rates.INDEX TERMS Knowledge discovery, data mining, clustering, spatial data, and topological relations.

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Section: Density-based Clustering Methodsmentioning

confidence: 99%

Enhanced Approach for Agglomerative Clustering Using Topological Relations

et al. 2023

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“…Zhu et al [26] applied the harmony search optimization algorithm to DBSCAN, and obtained better clustering parameters and better clustering results. Hu et al [27] proposed a density-based clustering algorithm, KR-DBSCAN, which is based on reverse nearest neighbor and influence space. Li et al [28] combined the improved DBSCAN algorithm based on bat optimization and DP algorithm for clustering, and obtained good results.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

Yang

Chen

et al. 2022

J Supercomput

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As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters.

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“…(4) If the radius of the inserted OMCp is less than or equal to the specified radius threshold (ϵ), it is merged, and its weights, radius and center are updated using operations similar to inserting PMC. (5) The new weights of the merged OMCp are further checked. If the weight is greater than βμ, OMCp is removed and a new PMC is formed.…”

Section: Merge Componentmentioning

confidence: 99%

“…The data in the same category should be as similar as possible, and those in different categories should be as different as possible [2]. The clustering techniques are generally divided into five categories: partition-based clustering [3,4], density-based clustering [5,6], hierarchy-based clustering [7,8], grid-based clustering [9,10] and model-based clustering [11,12]. The density-based clustering technology can handle clusters of arbitrary shape and can identify outliers, has gradually becomes a research hotspot [13,14].…”

Section: Introductionmentioning

confidence: 99%

DWDP-Stream: A Dynamic Weight and Density Peaks Clustering Algorithm for Data Stream

Chen

Zhou

et al. 2022

Int J Comput Intell Syst

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Identifying clusters of arbitrary shapes and constantly processing the newly arrived data points are two critical challenges in the study of clustering. This paper proposes a dynamic weight and density peaks clustering algorithm to simultaneously solve these two key issues. An online–offline framework is used, creating and maintaining micro-clusters in the online phase, and treating the micro-clusters as pseudo-points to form the final cluster in the offline phase. In the online phase, when a new data point is merged into the corresponding micro-cluster, a dynamic weight method is proposed to update the weight of the micro-cluster according to the distance between the point and the center of the micro-cluster, so as to more accurately describe the information of the micro-cluster. In the offline phase, the density peak clustering algorithm is improved, natural neighbors are introduced to adaptively obtain the local density of the data point, and the allocation process is improved to reduce the probability of allocation errors. The algorithm is evaluated on different synthetic and real-world datasets using different quality metrics. The experimental results show that the proposed algorithm improves the clustering quality in both static and streaming environments.

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KR-DBSCAN: A density-based clustering algorithm based on reverse nearest neighbor and influence space

Cited by 29 publications

References 20 publications

Enhanced Approach for Agglomerative Clustering Using Topological Relations

Enhanced Approach for Agglomerative Clustering Using Topological Relations

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

DWDP-Stream: A Dynamic Weight and Density Peaks Clustering Algorithm for Data Stream

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