A Fast Density and Grid Based Clustering Method for Data With Arbitrary Shapes and Noise

Wu, Bo; Wilamowski, Bogdan M.

doi:10.1109/tii.2016.2628747

Cited by 86 publications

(50 citation statements)

References 16 publications

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“…At the same time, KNN makes decisions based on the dominant categories of k objects, rather than on a single object category. These two points are the advantages of KNN algorithm [28]- [30].…”

Section: A Noval Density Peaks Clustering Halo Node Assignment Mmentioning

confidence: 90%

A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

et al. 2019

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The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clustering algorithm utilize the advantage of DBSCAN algorithm to quickly identify outliers, which improved the sensitivity to halo nodes. However, the identified halo nodes cannot be effectively assigned to adjacent clusters. Therefore, this paper will use K-nearest neighbor (KNN) algorithm to classify the identified halo nodes. K-nearest neighbor is the simplest and most efficient classification method. The KNN algorithm has the advantages of high accuracy, insensitivity to outliers and no input hypothesis data. Hence, we proposed a novel density peaks clustering halo node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC). KNN-HDPC can grasp the internal relations between outliers and cluster nodes more deeply, so as to dig out the deeper relations between nodes. Experimental results demonstrate that the proposed algorithm can effectively cluster and reclassify a large number of complex data. We can quickly dig out the potential relationship between noise points and cluster points. The improved algorithm has higher clustering accuracy than the original DPC algorithm, and essentially has more robust clustering results. INDEX TERMS Density peaks clustering, halo node, K-nearest neighbor, low-density nodes.

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Section: A Noval Density Peaks Clustering Halo Node Assignment Mmentioning

confidence: 90%

A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

et al. 2019

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“…Online or data stream clustering has attracted the attention of numerous researchers and analysts. In clustering data streams, an important issue is how to process this infinite data that are evolving over time or how to maintain the vast amount of data for later processing [24,47,48,65,66]. The literature has provided numerous methods that include data stream clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

et al. 2020

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In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The gridbased method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality. INDEX TERMS Clustering, data stream, evolving, grid-based method, core-micro-cluster, online.

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“…Data clustering techniques are effective tools to discover high-relevancy segments for extracting and analyzing the traffic information. Several clustering methods were widely used in data mining, such as partition-based clustering (PBC) [29], [30], density-based clustering (DBC) [31], [32], grid-based clustering (GBC) [33], [34], and hierarchy-based clustering (HBC) [35], [36]. These methods cluster data into different groups in accordance with the similarities, the distance between the data and cluster centers or the density of each group.…”

Section: A: Network Clusteringmentioning

confidence: 99%

Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

Guo

Fang

et al. 2019

IEEE Access

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Real-time traffic status information provides good references for urban traffic control and management. Travel time is easy to understand and widely employed in representing traffic status. With significantly improved positioning accuracy and coverage, trajectory data collected from GPS-equipped probe vehicles have great potential for traffic state recognition. This paper presents a machine learning enabled travel time estimation method based on the GPS-equipped probe vehicles data. This research considers the spatial-temporal relevancy while solving the travel time allocation problem: the travel time of target segment might be associated with its previous travel times and/or the traffic states of nearby relevant segments. After data normalization and network clustering, an artificial neural network (ANN) algorithm considering such spatial-temporal relevancy was conducted to infer the travel time distribution among the traveled segments within one path. Furthermore, a weighted summation of the travel time estimation result from various trajectories was calculated to better represent the segment travel time in one time step. The proposed method was evaluated by evaluating the estimation results with automatic vehicle identification obtained ground truth. The experimental results illustrated that by utilizing the ANN to consider the spatialtemporal relevancy, the proposed method is effective and efficient in estimating the travel time. INDEX TERMS Artificial neural network (ANN), data clustering, global positioning system (GPS) equipped probe vehicle, travel time estimation, travel time allocation.

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A Fast Density and Grid Based Clustering Method for Data With Arbitrary Shapes and Noise

Cited by 86 publications

References 16 publications

A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

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