An Improved DBSCAN Algorithm Based on the Neighbor Similarity and Fast Nearest Neighbor Query

Li, Shanshan

doi:10.1109/access.2020.2972034

Cited by 77 publications

(25 citation statements)

References 46 publications

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“…DBSCAN with neighbor similarity and fast nearest neighbor query [76] O(n( 1 seeds logn + minP ts 2 )) Aimed to remove the redundant region queries in the original DBSCAN through triangle inequality, neighbor similarity, and fast neighbor search algorithm. Uses a cover tree in its implementation.…”

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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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 DBSCAN algorithm is a spatial clustering algorithm based on density [ 39 ]. The remarkable advantage of this algorithm is that the algorithm is fast, and it can divide the regions with enough high density into a group, which effectively deals with noise points and quickly finds spatial clustering of arbitrary shape.…”

Section: Indoor Multiple Dynamic Objects Identification and Localimentioning

confidence: 99%

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

Liu

Wang

et al. 2020

Sensors

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In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

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“…Each subset is represented by a single representative point and the set of representatives is considered as the simplified data set [13]. Common rules include the distance functions or their L2 norm, such as k-means [14]- [16], DBSCAN [17]- [19], CURE [20], Chameleon [21], etc. The concept of shared nearest neighbor was introduced by Ertöz et al [22] to alleviate the problem that the distance functions do not work well in regions with varied densities and high dimensionality.…”

Section: Fig1 Application Framework Of Automatic Assembly Line Basedmentioning

confidence: 99%

Fast Assembly Tolerance Inspection Method Using Feature-Based Adaptive Scale Reduction in Automatic Assembly Line

Qiu

Zhang

et al. 2020

IEEE Access

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Enormous computation and long data processing time of the online tolerance inspection procedure has been a bottleneck in reducing the production cycle time of automatic assembly line. To reduce time consumption while maintaining accuracy, a fast assembly tolerance inspection method is proposed in this paper using scale-reduced models. First, the general part surface, input by a laser-scanned dense point cloud, is segmented through feature-based clustering. Shape variation estimation are designed to determine reduction ratios between clusters adaptively. Then, assembly simulation method is further advanced to utilize coarse point models with imbalanced density. Finally, numerical experiments are designed to estimate both static and dynamic assembly tolerances as results of tolerance inspection. A case study on a motion component of a rail material transporter shows that the proposed method could reduce 81% of the data processing time compared to traditional practice. The precision loss has minor influence on the inspection results because spatial positioning error is no more than 6% of tolerance interval and 18% of scanning resolution. Moreover, the proposed method has a comprehensive advantage of time and accuracy than two conventional point cloud simplification techniques based on iterative and clustering methods.

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An Improved DBSCAN Algorithm Based on the Neighbor Similarity and Fast Nearest Neighbor Query

Cited by 77 publications

References 46 publications

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

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

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

Fast Assembly Tolerance Inspection Method Using Feature-Based Adaptive Scale Reduction in Automatic Assembly Line

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