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An Improved density-based clustering algorithm based on Cellular Neural Networks

IEEE ACCESS 2024年 12卷 138027-138046页

作者： Xu, Jiexi Ling, Bingo Wing-Kuen Liu, Qing Wei, Yiting Zhang, Zhanbin Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China

clustering plays a major role in various disciplines including biological visual image classification. Various clustering algorithms have been introduced and developed. Among them, the density-based clustering algorithm achieves the best performance. The cellular neural network (CNN) based clustering algorithm (CNNCA) is a density-based method. It performs the clustering at a very high speed because the CNN can be implemented on a silicon chip. Nevertheless, the performance of the CNNCA is significantly degraded when facing clusters of complex structures, especially with significant density differences between clusters. In this paper, the local rules of the CNNs are modified. Moreover, the multi-spatial resolutions are employed. In particular, the values of minimum spatial resolution and the other parameters are adaptively designed. Hence, the iterative procedures can be applied to the remaining unassigned points. Furthermore, the conditional minimum distance rule is employed to assign the dispersed elements to the existing clusters. Different datasets have been utilized to evaluate the performance of our proposed method. Compared to the state-of-the-art clustering algorithms, the computer numerical simulation results show that our proposed method outperforms the existing state-of-the-art methods in higher accuracy and more robustness.

关键词： clustering algorithms Classification algorithms Convolutional neural networks Spatial resolution Partitioning algorithms Noise measurement Cellular neural networks Unsupervised learning clustering multi-spatial resolutions cellular neural networks density-based clustering algorithm unsupervised learning

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DBWGIE-MR: A density-based clustering algorithm by using the weighted grid and information entropy based on MapReduce

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 10781-10796页

作者： Yu, Xin Zeng, Feng Mwakapesa, Deborah Simon Nanehkaran, Y. A. Mao, Yi-Min Xu, Kai-Bin Chen, Zhi-Gang Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Jiangxi Peoples R China Cent South Univ Sch Comp Changsha 410083 Hunan Peoples R China

The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable division of data gridding, low accuracy of clustering results and low efficiency of parallelization in big data clustering algorithm based on density. This algorithm is implemented in three stages: data partitioning, local clustering, and global clustering. For each stage, we propose several strategies to improve the algorithm. In the first stage, based on the spatial distribution of data points, we propose an adaptive division strategy (ADG) to divide the grid adaptively. In the second stage, we design a weighted grid construction strategy (NE) which can strengthen the relevance between grids to improve the accuracy of clustering. Meanwhile, based on the weighted grid and information entropy, we design a density calculation strategy (WGIE) to calculate the density of the grid. And last, to improve the parallel efficiency, core clusters computing algorithm based on MapReduce (COMCORE-MR) are proposed to parallel compute the core clusters of the clustering algorithm. In the third stage, based on disjoint-set, we propose a core cluster merging algorithm (MECORE) to speed-up ratio the convergence of merged local clusters. Furthermore, based on MapReduce, a core clusters parallel merging algorithm (MECORE-MR) is proposed to get the clustering algorithm results faster, which improves the core clusters merging efficiency of the density-based clustering algorithm. We conduct the experiments on four synthetic clusters. Compared with H-DBSCAN, DBSCAN-MR and MR-VDBSCAN, the experimental results show that the DBWGIE-MR algorithm has higher stability and accuracy, and it takes less time in parallel clustering.

关键词： Big data density-based clustering algorithm weighted grid information entropy

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MR-DBIFOA: a parallel density-based clustering algorithm by Using Improve Fruit Fly Optimization

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电脑学刊 2022年第1期33卷 101-114页

作者： Wei Liu Jiaxin Wang Xiaopan Su Yimin Mao

clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density- based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clustering algorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.

关键词： density-based clustering algorithm KD-Tree MR-DBIFOA fruit fly optimization

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An Efficient density-based clustering algorithm for the Capacitated Vehicle Routing Problem

An Efficient Density-Based Clustering Algorithm for the Capa...

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International Conference on Computer Network, Electronic and Automation (ICCNEA)

作者： Zhang, Jiashan Chongqing Vocat Inst Engn Chongqing 402260 Peoples R China

ISBN: (纸本)9781538639818

The capacitated vehicle routing problem (CVRP) is one of the most challenging problems in the optimization of distribution. Most approaches can solve case studies involving less than 100 nodes to optimality, but time-consuming. To overcome the limitation, this paper presents a novel two-phase heuristic approach for the capacitated vehicle routing problem. Phase I aims to identifying sets of cost-effective feasible clusters through an improved density-based clustering algorithm. Phase II assigns clusters to vehicles and sequences them on each tour. Max-min ant system is used to order nodes within clusters. The simulation results indicate efficiency of the proposed algorithm.

关键词： CVRP Two-phase heuristic density-based clustering algorithm Max-min ant system

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Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2016年第Oct.期73卷 14-22页

作者： Kamali, Tahereh Stashuk, Daniel Univ Waterloo Syst Design Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada

Objective: Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Methods and material: Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. Results: The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). Conclusions: NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering nat

关键词： density-based clustering algorithm Chameleon DBSCAN Spectral clustering White matter fiber bundles segmentation

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An Enhanced density-based clustering algorithm for the Autonomous Indoor Localization

An Enhanced Density-based Clustering Algorithm for the Auton...

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International Conference on MOBILe Wireless MiddleWARE, Operating Systems and Applications (Mobilware)

作者： Xu, Yaqian Kusber, Rico David, Klaus Univ Kassel Chair Commun Technol D-34119 Kassel Germany

ISBN: (纸本)9781479922864

Indoor localization applications are expected to become increasingly popular on smart phones. Meanwhile, the development of such applications on smart phones has brought in a new set of potential issues (e.g., high time complexity) while processing large datasets. The study in this paper provides an enhanced density-based cluster learning algorithm for the autonomous indoor localization algorithm DCCLA (density-based clustering Combined Localization algorithm). In the enhanced algorithm, the density-based clustering process is optimized by "skipping unnecessary density checks" and "grouping similar points". We conducted a theoretical analysis of the time complexity of the original and enhanced algorithm. More specifically, the run times of the original algorithm and the enhanced algorithm are compared on a PC (personal computer) and a smart phone, identifying the more efficient density-based clustering algorithm that allows the system to enable autonomous Wi-Fi fingerprint learning from large Wi-Fi datasets. The results show significant improvements of run time on both a PC and a smart phone.

关键词： Time complexity of algorithms Run time of algorithms density-based clustering algorithm Fingerprinting-based indoor localization

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A Group Construction algorithm based on density and Closeness clustering in Mobile Communication Networks 14

A Group Construction Algorithm Based on Density and Closenes...

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14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

作者： Li, Jie Guo, Yue Li, Tengfei Yu, Ruiyun Wang, Xingwei Northeastern Univ Sch Comp Sci & Engn Shenyang Liaoning Peoples R China Northeastern Univ Software Coll Shenyang Liaoning Peoples R China

ISBN: (纸本)9781728105482

with the strong impact of OTT (Over The Top) business in the mobile Internet era, operators urgently need to discover the user value information from massive data to help them provide personalized accurate services and expand business customer services. The construction of social user groups based on mobile communication data can help operators to accurately analyze customer social structures, thus promoting quality service and improving marketing quality. In this paper, we design a set of social group construction algorithm based on user behavior characteristics excavated from massive user data in mobile communication network. Due to the huge volume of mobile communication data sets, a parallel design based on MapReduce is exploited. The experimental results show that the ADBLINKw algorithm performs well on the efficiency and community detection quality.

关键词： mobile communication network communication social relationship measurement group structure density-based clustering algorithm complex network

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Use of Time-Frequency map combined with DBSCAN algorithm for separation of partial discharge pulses under DC voltage

Use of Time-Frequency map combined with DBSCAN algorithm for...

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IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP)

作者： Di Fatta, Alessio Romano, Pietro Imburgia, Antonino Ala, Guido Rizzo, Giuseppe Vigni, Vincenzo Li Albertini, Marco Bononi, Stefano Franchi Univ Palermo Dept Engn LEPRE HV Lab Palermo Italy Prysmian Elect Palermo Italy Prysmian Grp Milan Italy

ISBN: (纸本)9781665467957

The Phase-Resolved-Partial-Discharge pattern (PRPD) is a conventional technique used for the evaluation of partial discharges (PD) phenomena in High-VoltageAlternating-Current (HVAC) systems. This map is constructed by plotting the peak of each detected pulses as a function of the phase angle of the supply voltage. Therefore it is obvious that this technique cannot be used for the analysis of data from PD mesaurement under different supply voltage condition (DC). The aim of this paper is to evaluate the application of the Time-Frequency map (TF map) for the analysis of a dataset obtained from PD measurement under DC voltage. A density-based clustering algorithm was also used to gain more insight from the collected data. The results show that, with this approach, it's possible to perform a noise rejection and identify PD pulses.

关键词： Partial Discharge PD Time-Frequency map density-based clustering algorithm

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Application of Unsupervised Learning algorithms in Environmental Factor Correlation Analysis and Model Construction

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Procedia Computer Science 2025年 261卷 148-157页

作者： Yu Zhao Ting Zhang Xinjiang Shengyuan Xianghe Environmental Protection Engineering Co. Ltd. Urumqi Xinjiang China

As environmental protection issues become more serious, scientists and policy makers are in urgent need of efficient tools to analyze and manage environmental data. This study explores the application of unsupervised learning algorithms in correlation analysis and model building, with a special focus on the effectiveness of clustering, dimensionality reduction and density estimation algorithms in processing and interpreting complex environmental data. By applying K-mean clustering, Principal Component Analysis (PCA), self-organizing mapping, and density-based Spatial clustering of Applications with Noise (DBSCAN), we identified air and water pollution sources, assessed the environmental quality of multidimensional environmental data, monitored the health of ecosystems, and evaluated environmental risk areas. Higher concentrations of PM2.5 (2.5-micrometer Particulate Matter), NO2, and SO2 in the industrial areas (monitoring points 1, 3, 5, 7, and 10) indicate that there are serious air pollution problems in these areas. The results show that the unsupervised learning algorithm can effectively extract key information from environmental monitoring data to support the formulation and implementation of environmental policies. This study not only extends the application of unsupervised learning in the field of environmental sciences, but also provides a reliable data-driven method for environmental management, which helps to improve the science and effectiveness of environmental protection and sustainable development.

关键词： Unsupervised Learning algorithm Environmental Factor Correlation Analysis Model Construction density-based clustering algorithm

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GNN-DBSCAN: A new density-based algorithm using grid and the nearest neighbor

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期41卷 7589-7601页

作者： Li Yihong Wang Yunpeng Li Tao Lan Xiaolong Song Han Sichuan Univ Sch Cyber Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used densitybased clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts. Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN.

关键词： density-based clustering algorithm Grid The nearest neighbor DBSCAN

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