scholarly journals An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4920
Author(s):  
Lin Cao ◽  
Xinyi Zhang ◽  
Tao Wang ◽  
Kangning Du ◽  
Chong Fu
Keyword(s):  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Search Algorithm ◽  
Measurement Data ◽  
Data Sets ◽  
Density Peak ◽  
Close Range ◽  
Decision Graph ◽  
Membership Matrix

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.

DBSCANI: Noise-Resistant Method for Missing Value Imputation

2016 ◽  
Vol 25 (3) ◽  
pp. 431-440 ◽  
Author(s):  
Archana Purwar ◽  
Sandeep Kumar Singh
Keyword(s):  
Spatial Data ◽  
Missing Values ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Data Sets ◽  
Quality Of Data ◽  
Data Set ◽  
Dbscan Clustering ◽  
Density Based Clustering

AbstractThe quality of data is an important task in the data mining. The validity of mining algorithms is reduced if data is not of good quality. The quality of data can be assessed in terms of missing values (MV) as well as noise present in the data set. Various imputation techniques have been studied in MV study, but little attention has been given on noise in earlier work. Moreover, to the best of knowledge, no one has used density-based spatial clustering of applications with noise (DBSCAN) clustering for MV imputation. This paper proposes a novel technique density-based imputation (DBSCANI) built on density-based clustering to deal with incomplete values in the presence of noise. Density-based clustering algorithm proposed by Kriegal groups the objects according to their density in spatial data bases. The high-density regions are known as clusters, and the low-density regions refer to the noise objects in the data set. A lot of experiments have been performed on the Iris data set from life science domain and Jain’s (2D) data set from shape data sets. The performance of the proposed method is evaluated using root mean square error (RMSE) as well as it is compared with existing K-means imputation (KMI). Results show that our method is more noise resistant than KMI on data sets used under study.

scholarly journals FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy

2020 ◽  
Vol 16 (12) ◽  
pp. e1008479
Author(s):  
Daniel F. Nino ◽  
Daniel Djayakarsana ◽  
Joshua N. Milstein
Keyword(s):  
Single Molecule ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Cluster Complex ◽  
Nuclear Pore ◽  
Data Sets ◽  
Localization Microscopy ◽  
Biological Dataset ◽  
Open Source Software Package ◽  
Single Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) is a powerful tool for studying intracellular structure and macromolecular organization at the nanoscale. The increasingly massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Here we present FOCAL3D, an accurate, flexible and exceedingly fast (scaling linearly with the number of localizations) density-based algorithm for quantifying spatial clustering in large 3D SMLM data sets. Unlike DBSCAN, which is perhaps the most commonly employed density-based clustering algorithm, an optimum set of parameters for FOCAL3D may be objectively determined. We initially validate the performance of FOCAL3D on simulated datasets at varying noise levels and for a range of cluster sizes. These simulated datasets are used to illustrate the parametric insensitivity of the algorithm, in contrast to DBSCAN, and clustering metrics such as the F1 and Silhouette score indicate that FOCAL3D is highly accurate, even in the presence of significant background noise and mixed populations of variable sized clusters, once optimized. We then apply FOCAL3D to 3D astigmatic dSTORM images of the nuclear pore complex (NPC) in human osteosaracoma cells, illustrating both the validity of the parameter optimization and the ability of the algorithm to accurately cluster complex, heterogeneous 3D clusters in a biological dataset. FOCAL3D is provided as an open source software package written in Python.

A Finding Method of Shape Theme Based on Wavelet Time Series

2013 ◽  
Vol 756-759 ◽  
pp. 3199-3203
Author(s):  
Qiu Guo ◽  
Lu Guo
Keyword(s):  
Euclidean Distance ◽  
Search Algorithm ◽  
Real Data ◽  
Data Sets ◽  
Real Time Processing ◽  
The Real ◽  
Multi Resolution Analysis ◽  
Processing Ability ◽  
Resolution Analysis ◽  
Finding Method

Finding shape theme has raised great attention in the database of shapes. According to the problem of incompatible about accuracy and complexity in the shape theme search algorithm ,this paper proposed a finding theme algorithm using the multi-resolution analysis of wavelet and the processing capability of reduction dimension of time sequence , accurately calculated the similarity between different objects combining with the Euclidean distance formula, and achieved satisfactory results. Through the comparison between the real data sets to test and traditional shape theme algorithm, it shows that the method has good stability and reliability, and ensure the real-time processing ability of the closed contour shapes overall matching.

Lidar and radar fusion for real-time road-objects detection and tracking

2021 ◽  
pp. 1-14
Author(s):  
Wael Farag
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Performance ◽  
Clustering Algorithm ◽  
Unscented Kalman Filter ◽  
Spatial Clustering ◽  
Measurement Data ◽  
Autonomous Driving ◽  
Geometrical Shape ◽  
Detection And Tracking

In this paper, based on the fusion of Lidar and Radar measurement data, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects’ positions but significantly less accurate on measuring their velocities. However, Radars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. Therefore, the merits of both sensors are combined using the proposed fusion approach to provide both pose and velocity data for objects moving in roads precisely. The Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) clustering algorithm is used to detect objects and estimate their centroids from the lidar and radar raw data. Then, the estimation of the object’s velocity as well as determining its corresponding geometrical shape is performed by the RANdom SAmple Consensus (RANSAC) algorithm. The proposed technique is implemented using the high-performance language C+⁣+ and utilizes highly optimized math and optimization libraries for best real-time performance. The performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians.

Urban green economic development indicators based on spatial clustering algorithm and blockchain

2020 ◽  
pp. 1-12
Author(s):  
Xiaoguang Gao
Keyword(s):  
Development Strategy ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Large Data ◽  
Experimental Comparison ◽  
High Dimensional ◽  
Density Peak ◽  
Data Set

The unbalanced development strategy makes the regional development unbalanced. Therefore, in the development process, resources must be effectively utilized according to the level and characteristics of each region. Considering the resource and environmental constraints, this paper measures and analyzes China’s green economic efficiency and green total factor productivity. Moreover, by expounding the characteristics of high-dimensional data, this paper points out the problems of traditional clustering algorithms in high-dimensional data clustering. This paper proposes a density peak clustering algorithm based on sampling and residual squares, which is suitable for high-dimensional large data sets. The algorithm finds abnormal points and boundary points by identifying halo points, and finally determines clusters. In addition, from the experimental comparison on the data set, it can be seen that the improved algorithm is better than the DPC algorithm in both time complexity and clustering results. Finally, this article analyzes data based on actual cases. The research results show that the method proposed in this paper is effective.

scholarly journals An Adaptive Sweep-circle Spatial Clustering Algorithm Based on Gestalt

2017 ◽  
Author(s):  
Qingming Zhan ◽  
Shuguang Deng ◽  
Zhihua Zheng
Keyword(s):  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Streaming ◽  
Spatial Clusters ◽  
Streaming Technology ◽  
Threshold Setting

An adaptive spatial clustering (ASC) algorithm is proposed that employs sweep-circle techniques and a dynamic threshold setting based on Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of priori knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering large data sets.

Detecting tourist attractions using geo-tagged photo clustering

2018 ◽  
Vol 3 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Wenyuan Zhang ◽  
Guoxin Tan ◽  
Ming Lei ◽  
Xiaomei Guo ◽  
Chuanming Sun
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Local Density ◽  
Gaussian Kernel ◽  
Tourist Attractions ◽  
Photo Sharing ◽  
Data Points ◽  
Decision Graph ◽  
Photo Clustering

Millions of geo-tagged photos are becoming available due to the wide spread of photo-sharing websites, which provide valuable information to mine spatial patterns from human activities. In this study, we present a simple and fast density-based spatial clustering algorithm to detect popular scenic spots using geo-tagged photos collected from Flickr. In this algorithm, Gaussian kernel is applied to estimate local density of data points, and a decision graph is used to obtain cluster centers easily. More than 289,000 geo-tagged photos located in five typical cities of China are downloaded as case studies, and data pre-processing such as duplicate removing is performed to improve the quality of clustering result. Finally, popular tourist attractions of each sample city are successfully detected with this algorithm, and our result is useful for recommending some interesting destinations which might not be on the list of tourist website or mobile guide applications. The proposed solution is robust with respect to different distributions of photos, and it is efficient by comparing with other popular clustering approaches.

scholarly journals Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Qi Diao ◽  
Yaping Dai ◽  
Qichao An ◽  
Weixing Li ◽  
Xiaoxue Feng ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Local Density ◽  
Search Algorithm ◽  
Real Data ◽  
Nearest Neighbors ◽  
Adjusted Rand Index ◽  
Clustering Methods ◽  
K Nearest Neighbors ◽  
Density Peaks

This paper presents an improved clustering algorithm for categorizing data with arbitrary shapes. Most of the conventional clustering approaches work only with round-shaped clusters. This task can be accomplished by quickly searching and finding clustering methods for density peaks (DPC), but in some cases, it is limited by density peaks and allocation strategy. To overcome these limitations, two improvements are proposed in this paper. To describe the clustering center more comprehensively, the definitions of local density and relative distance are fused with multiple distances, including K-nearest neighbors (KNN) and shared-nearest neighbors (SNN). A similarity-first search algorithm is designed to search the most matching cluster centers for noncenter points in a weighted KNN graph. Extensive comparison with several existing DPC methods, e.g., traditional DPC algorithm, density-based spatial clustering of applications with noise (DBSCAN), affinity propagation (AP), FKNN-DPC, and K-means methods, has been carried out. Experiments based on synthetic data and real data show that the proposed clustering algorithm can outperform DPC, DBSCAN, AP, and K-means in terms of the clustering accuracy (ACC), the adjusted mutual information (AMI), and the adjusted Rand index (ARI).

scholarly journals An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

2017 ◽  
Author(s):  
Qingming Zhan ◽  
Shuguang Deng ◽  
Zhihua Zheng
Keyword(s):  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Streaming ◽  
Spatial Clusters ◽  
Streaming Technology ◽  
Threshold Setting

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

Multi-objective analysis of clusters using gravitational density based model with PSO

2019 ◽  
Vol 12 ◽  
Author(s):  
Naveen Trivedi ◽  
Suvendu Kanungo
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Gravitational Search Algorithm ◽  
Vital Role ◽  
Computational Time ◽  
Data Sets ◽  
Multi Objective ◽  
Cluster Distance

Background: Clustering analysis plays a vital role in obtaining knowledgeable data from the huge amount of data sets in knowledge discovery. Most of the traditional clustering algorithms do not work well with high dimensional data. The objective of effective clustering is to obtain well connected, compact and separated clusters. Density-based clustering (DBSCAN) is one of the popular clustering algorithms uses local density information of data points to detect clusters with arbitrary shapes. The Gravitational search algorithm (GSA) is one of the effective approaches inspired by Newton’s law of gravitational force where every particle in the universe attracts every other particle with force. Objectives: In this paper, a novel multi-objective clustering is proposed to produce the desired number of valid clusters, further in a part of the paper we have also optimized the algorithm to obtain optimal solutions. Method: In the proposed approach a hybrid clustering algorithm based on GSA along with DBSCAN is recommended to group the data into the desired number of clusters, and in the next phase of the algorithm PSO is applied in order to optimize the solutions using the fitness functions. Results: In the analysis of the result, we used two objectives function namely quantization error and inter-cluster distance to evaluate the performance of our algorithm. Conclusion: The algorithm has been compared with some well- known traditional heuristics based method in terms of accuracy and computational time.

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