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A novel projection-based distance measure for interval-valued intuitionistic multiplicative clustering algorithm

SOFT COMPUTING 2023年第5期27卷 2369-2383页

作者： Liu, Ming Zhao, Hua Xu, Zeshui Cui, Feng Natl Univ Def Technol Res Inst 63 Nanjing 210007 Peoples R China Army Engn Univ PLA Dept Gen Educ Nanjing 210007 Peoples R China Sichuan Univ Business Sch State Key Lab Hydraul & Mt River Engn Chengdu 610064 Peoples R China Nanjing Agr Univ Coll Humanities & Social Sci Nanjing 210095 Peoples R China

Distance measure is an effective tool for describing the difference between two vectors. Many scholars have proposed a lot of distance measures between the intuitionistic fuzzy sets. However, there are few works about the interval-valued intuitionistic multiplicative (IVIM) distance measure. The few research achievements are not sufficient to deal with the problems involving the distance between two interval-valued intuitionistic multiplicative sets (IVIMSs). Thus, there still exist some shortages in fully describing the difference between two IVIMSs. In this paper, we first propose an improved distance measure, the projection-based distance measure, which can reflect the difference between two objects more accurately with IVIM information. After that, a new method is introduced to determine the experts' weights based on the projection-based distance measure. Then, to handle the group decision making problem in which the weights of experts are unknown, we use the proposed projection-based distance measure to construct the similarity matrix in Boole clustering method. Finally, the clustering method is applied to the customer classification problem to test the reliability of the method.

关键词： Interval-valued intuitionistic multiplicative set Projection-based distance measure Expert weights determination clustering algorithm Group of experts' opinions

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The computer desktop image compression based on clustering algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第13期34卷 e5892-e5892页

作者： Gui, Dawei Shanxi Business Coll Inst Informat & Intelligent Technol Xian 710119 Shanxi Peoples R China

With the rapid development of computer software and hardware technology and network technology, aiming at the limitations of the traditional image compression standards and the deficiencies of the existing computer desktop image compression methods. By analyzing the characteristics of computer desktop image, according to the characteristics of desktop compression, a compression scheme based on high efficiency video coding (HEVC) and color clustering is proposed, which divides blocks into text/graphics blocks, natural image blocks and mixed blocks based on the features of histogram information and texture information of blocks. In block division and classification, an adaptive dynamic block classification algorithm is proposed which is different from the traditional block partitioning. Compared with the traditional method, the new block classification algorithm can save the code stream and improve the classification accuracy.

关键词： clustering algorithm color clustering compression scheme computer image compression

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On an improved clustering algorithm based on node density for WSN routing protocol

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第4期25卷 3005-3017页

作者： Chang, Luyao Li, Fan Niu, Xinzheng Zhu, Jiahui Univ Elect Sci & Technol China Comp Sci & Engn Chengdu Sichuan Peoples R China

To better collect data in context to balance energy consumption, wireless sensor networks (WSN) need to be divided into clusters. The division of clusters makes the network become a hierarchical organizational structure, which plays the role of balancing the network load and prolonging the life cycle of the system. In clustering routing algorithm, the pros and cons of clustering algorithm directly affect the result of cluster division. In this paper, an algorithm for selecting cluster heads based on node distribution density and allocating remaining nodes is proposed for the defects of cluster head random election and uneven clustering in the traditional LEACH protocol clustering algorithm in WSN. Experiments show that the algorithm can realize the rapid selection of cluster heads and division of clusters, which is effective for node clustering and is conducive to equalizing energy consumption.

关键词： clustering algorithm LEACH protocol WSN

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Adaptive Partitioning by Local Density-Peaks: An Efficient Density-Based clustering algorithm for Analyzing Molecular Dynamics Trajectories

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2017年第3期38卷 152-160页

作者： Liu, Song Zhu, Lizhe Sheong, Fu Kit Wang, Wei Huang, Xuhui Hong Kong Univ Sci & Technol Dept Chem Clear Water Bay Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Ctr Syst Biol & Human Hlth State Key Lab Mol Neurosci Clear Water Bay Kowloon Hong Kong Peoples R China

We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. (C) 2016 Wiley Periodicals, Inc.

关键词： clustering algorithm kNN search density peaks molecular dynamics Markov State Models

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A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction

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INFORMATION SCIENCES 2017年 381卷 104-123页

作者： Laohakiat, Sirisup Phimoltares, Suphakant Lursinsap, Chidchanok Chulalongkorn Univ Dept Math & Comp Sci Bangkok Thailand

We present an algorithm for clustering high dimensional streaming data. The algorithm incorporates dimension reduction into the stream clustering framework. When a new datum arrives, the algorithm performs dimension reduction to find a local projected subspace using unsupervised LDA (Linear Discriminant Analysis)-based method. The obtained local subspace would maximally separate the nearby micro-clusters with respect to the incoming point. Then, the incoming point is assigned to a micro-cluster in the projected space, rather than in the full dimensional space. The experimental results show that the proposed algorithm outperforms its counterpart streaming clustering algorithms. Moreover, when compared with traditional clustering algorithms which require the whole data set, the proposed algorithms shows comparable clustering performances with much less computation time for large data sets. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Stream data clustering Dimension reduction Linear discriminant analysis clustering algorithm Linear discriminant analysis subspace

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Pixel-Level clustering algorithm for Data Reconstruction in ILC

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2023年第12期70卷 2622-2629页

作者： Zhao, R. Zheng, R. Wei, X. Xue, F. Wang, J. Zhao, Y. Hu-Guo, C. Hu, Y. Northwestern Polytech Univ Inst Microelect Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Nankai Univ Coll Elect Informat & Opt Engn Tianjin 300350 Peoples R China Inst Pluridisciplinaire Hubert CURIEN F-67037 Strasbourg France

A high amount of hits on the vertex detector of the International Linear Collider (ILC) are generated by the beam background, which leads to an increase in the data flow of the detector system. Charged particles coming from the beam background have low momentum, resulting in the generation of elongated clusters. The CMOS pixel sensor (CPS), which integrates pre-processing functions and on-chip artificial neural networks (ANNs), could remove these elongated clusters. clustering is the first step for data pre-processing and is used to collect clusters from raw data. In this article, a pixel-level clustering algorithm with a 5 chi 5 window executed in real time is proposed. The algorithm is tested using 4500 frames (500 frames for each angle of incidence) of raw data (12 bits/pixel) from a MIMOSA-18 sensor and compared to conventional clustering algorithms. The clustering implementation for an example array of 5 chi 5 pixels is synthesized for different frequencies (100 and 200 MHz) and analog-to-digital converter (ADC) resolutions (4 and 8 bits). The power dissipation and occupied area of the different implementations are analyzed. The hardware implementation of the algorithm provides the possibility to integrate the clustering function into the CPS.

关键词： clustering algorithm CMOS pixel sensor (CPS) pixel level vertex detector

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Event Message clustering algorithm for Selection of Majority Message in VANETs

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IEEE ACCESS 2023年 11卷 14621-14635页

作者： Khatri, Narayan Lee, Sihyung Mateen, Abdul Nam, Seung Yeob Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Fed Urdu Univ Arts Sci & Technol Dept Comp Sci Islamabad 45570 Pakistan

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for disseminating truthful event messages. False messages may cause vehicles to behave in unintended ways, creating an unreliable transportation system. The efficiency and reliability of the transportation system can be obtained through trustworthy vehicular nodes providing correct event messages. In a VANET, the consensus issue can be resolved by employing blockchain. Even if we employ blockchain in a VANET, the trustworthiness of each message recorded needs to be verified separately since the blockchain itself does not guarantee the trust level of each event message. For instance, when there are multiple conflicting messages associated with a single accident on the road, a vote based on majority opinion can be considered one option for making a decision regarding the accident. In this work, we design the VANET event message clustering algorithm (VEMCA) to resolve the conflicting message problem. Furthermore, we develop a simulator for the VANET environment that demonstrates how the clustering algorithm can be used for event message validation. Experimental results show that our algorithm outperforms state-of-the-art clustering algorithms in terms of accuracy, precision, recall, f1-score, and computational time.

关键词： Vehicular ad hoc networks Peer-to-peer computing clustering algorithms Blockchains Trust management Safety Road traffic VANET clustering algorithm trustworthiness blockchain simulator

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The Design of clustering algorithm and MAC Protocol for Low Delay Underwater Acoustic Sensor Networks

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IEEE SENSORS JOURNAL 2023年第3期23卷 3251-3261页

作者： Zhang, Qinzheng Wang, Haiyan Zhao, Ruiqin Shen, Xiaohong He, Ke Zhang, Hongwei Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China

Underwater acoustic sensor networks (UASNs), which are popular in various application fields, including marine resources development, environmental exploration, seismic monitoring, etc., have made great progress in recent years. To maintain good scheduling performance, clustering algorithms and multiple access control (MAC) protocols have been widely used in sensor networks to improve network efficiency. However, the existing algorithms and protocols still have many shortcomings. For example, many clustering algorithms consider the delay performance little, the cluster structures are not always fully utilized by MAC protocols, and the cluster maintenance strategies are not considered. This article is devoted to solving those problems. By taking the node traffic and distances into account simultaneously, we design the cluster structure reasonably. And based on this structure, we plan a conflict-free handshake protocol with minimal idle time gaps. Besides, we also design a joining-cluster strategy for the free nodes to maintain the network without interference. Simulation results show that our work can perform well in network uniformity and end-to-end delay.

关键词： Delays clustering algorithms Sensors Protocols Media Access Protocol Underwater acoustics Mathematical models clustering algorithm low delay multiple access control (MAC) protocol underwater acoustic sensor network (UASN)

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Research on Adaptive Intelligent Decision algorithm in Industry 4.0 Digital Economy and Management Transformation Based on clustering algorithm

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第3期31卷 870-880页

作者： Zhang, Yanzhi Zhuang, Guoping Lv, Haiyang Li, Qinghua Yantai Nanshan Univ Coll Technol & Data Yantai 265713 Peoples R China Fushan 1 Senior High Yantai 265599 Peoples R China Yantai Nanshan Univ Coll Econ & Management Longkou 265713 Shandong Peoples R China

How to transform the business model of enterprises and make it more in line with the new requirements of industrial upgrading is an important part of the government's supply-side reform in the era of Industry 4.0. For the current research on the good management transformation of digital economy in the fourth Industrial revolution, linear function and four commonly used nonlinear function models are first selected to test the good management transformation of digital economy in different technologies, and the key success factors of the strategic transformation from traditional industry to Industry 4.0 business model are explored based on fuzzy hierarchical analysis. Logistic and Gompertz models were used to judge the life cycle stage of the hot technologies of the fourth Industrial revolution, and based on their development trend clustering, the hot technology groups of the fourth Industrial revolution were explored. Secondly, k-means clustering algorithm based on the optimal class center perturbation is proposed, and simulation experiments are carried out. A clustering algorithm based on k-means algorithm is designed, and the moving mode of k-means in the algorithm is changed, and a disturbance strategy is added to strengthen the adaptive intelligent decision-making ability of the algorithm. K-means clustering algorithm based on the optimal class center disturbance is proposed, and simulation experiments are conducted. At the same time, the values of step size factors in the algorithm are compared experimentally. Finally, it explores the function mechanism of digital economy, management transformation and management transformation in the operation of supply chain node enterprises. By analyzing the current situation and problems of digital operation of logistics and supply chain enterprises, it constructs the digital operation system of supply chain enterprises, and proposes the path of digital transformation and upgrading of supply chain enterprises, so as to promot

关键词： adaptive intelligent decision making clustering algorithm digital economy industry 4.0 management transformation

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A clustering algorithm using skewness-based boundary detection

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NEUROCOMPUTING 2018年 275卷 618-626页

作者： Li, Xiangli Han, Qiong Qiu, Baozhi Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Henan Peoples R China

clustering analysis has been applied in all aspects of data mining. Density-based and grid-based clustering algorithms are used to form clusters from the core points or dense grids to extend to the boundary of the clusters. However, deficiencies are still existed. To find out the right boundary and improve the precision of the cluster, this paper has proposed a new clustering algorithm (named C-USB) based on the skew characteristic of the data distribution in the cluster margin region. The boundary degree calculated by skew degree and the local density are used to distinguish whether a data is an internal point or non-internal point. And the connected matrix is constructed by removing the neighbor relationships of non-internal points from the relationships of all points, then the clusters can be formed by searching from the connected matrix towards internal of the clusters. Experimental results on synthetic and real data sets show that the C-USB has higher accuracy than that of similar algorithms. (C) 2017 Published by Elsevier B.V.

关键词： Skewness Boundary degree clustering algorithm clustering boundary

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