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Random Feature-Based Collaborative Kernel Fuzzy clustering for Distributed Peer-to-Peer Networks

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第2期31卷 692-706页

作者： Wang, Yingxu Han, Shiyuan Zhou, Jin Chen, Long Chen, C. L. Philip Zhang, Tong Liu, Zhulin Wang, Lin Chen, Yuehui Univ Jinan Shandong Prov Key Lab Network Based Intelligent Co Jinan 250022 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 999078 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Kernel clustering has the ability to get the inherent nonlinear structure of the data. But the high computational complexity and the unknown representation of the kernel space make it unavailable for the data clustering in distributed peer-to-peer (P2P) networks. To solve this issue, we propose a new series of random feature-based collaborative kernel clustering algorithms in this article. In the most basic algorithm, each node in a distributed P2P network first maps its data into a low-dimensional random feature space with the approximation of the given kernel by using the random Fourier feature mapping method. Then, each node independently searches the clusters with its local data and the collaborative knowledge from its neighbor nodes, and the distributed clustering is performed among all network nodes until reaching the global consensus result, i.e., all nodes have the same cluster centers. In addition, an improved version is designed with assignment of feature weights, which is optimized by the maximum-entropy technique to extract important features for the cluster identification. What's more, to relief the impact of different kernel functions and related parameters on clustering results, the combination of multiple kernels rather than a single kernel is adopted for the low-dimensional approximation, and the optimized weights are assigned to provide the guidance on the choice of the kernels and their parameters and discover significant features at the same time. Experiments on synthetic and real-world datasets show that the proposed methods achieve similar and even better results than the traditional kernel clustering methods on various performance metrics, including the average classification rate, the average normalized mutual information, and the average adjusted rand index. More importantly, the low-dimensional random features approximated to kernels and the distributed clustering mechanism adopted in these methods bring the greatly lower temporal complexit

关键词： Kernel Distributed databases Peer-to-peer computing Collaboration clustering algorithms clustering methods Prototypes Collaborative distributed clustering feature weights kernel fuzzy clustering random Fourier feature

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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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Transfer Learning-Based Collaborative Multiview clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第4期31卷 1163-1177页

作者： Liu, Xiangdao Wang, Rongrong Zhou, Jin Chen, C. L. Philip Zhang, Tong Chen, Yuehui Han, Shiyuan Du, Tao Ji, Ke Zhang, Kun Univ Jinan Shandong Prov Key Lab Network Based Intelligent Co Jinan 250022 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Collaborative multiview clustering methods can efficiently realize the view fusion by exploring complementary and consistent information among multiple views. However, these studies ignore all the differences between multiple views in fusion. In fact, in the multiview clustering, the data are diverse from view to view. The larger the difference between any two views is, the more the fusion of these views is required. Moreover, a global tradeoff parameter is generally adopted to restrain the penalty related to the disagreement of all views, which is often defined empirically. Inspired by the idea of transfer learning, a series of novel collaborative multiview clustering algorithms are proposed to tackle these challenges. In the most basic one, each view performs clustering independently and learns from others to improve its own clustering performance, in which a global learning factor is defined to control the interaction between multiple views. The fuzzy memberships are regarded as the important knowledge to provide guidance between views, and the consensus constraint is defined to ensure the consistent partitions of all views. In addition, the local adaptive learning factors between any two views instead of a global fixed one are adopted in an improved version to emphasize the difference between views, and the adjustment strategy for the learning factor is further designed to guarantee the stability of multiview clustering without the influence of initial values. Finally, to identify the significance of different views to the clustering, the extended versions are excavated with the assignment of view weights and the maximum entropy regularization technique is employed to optimize the weights. Experiments on various real-world multiview datasets verify the superiority of the presented approaches.

关键词： Collaboration clustering algorithms Transfer learning Partitioning algorithms clustering methods Entropy Adaptive learning Adaptive learning factor adjustment collabo- rative multiview clustering entropy regularization transfer clustering

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A Sparse Framework for Robust Possibilistic K-Subspace clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第4期31卷 1124-1138页

作者： Zeng, Shan Duan, Xiangjun Li, Hao Bai, Jun Tang, Yuanyan Wang, Zhiyong Wuhan Polytech Univ Coll Math & Comp Sci Wuhan Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia Univ Macau Fac Sci & Technol Macau Peoples R China Univ Sydney Sch Comp Sci Sydney NSW Australia

clustering noisy, high-dimensional, and structurally complex data have always been a challenging task. As most existing clustering methods are not able to deal with both the adverse impact of noisy samples and the complex structures of data, in this article, we propose a novel robust and sparse possibilistic K-subspace (RSPKS) clustering algorithm to integrate subspace recovery and possibilistic clustering algorithms under a unified sparse framework. First, the proposed method sparsifies the membership matrix and the subspace projection vector under a dual-sparse framework to handle high-dimensional noisy data. This unifies dimensionality reduction and clustering using one objective function for which the optimization can be realized through synchronous iteration. Second, the reconstruction error of each sample in the local subspace is used as the distance metric for classification. That is, each sample itself is treated as a clustering prototype so as not to be affected by the structure of the overall data distribution. Therefore, the clustering prototype construction problem of the data with complex structures can be better addressed. Finally, to deal with nonlinear regions, our RSPKS method is further extended into a kernelized version, namely the kernelized RSPKS clustering algorithm. The experimental results on both synthetic and real-world datasets demonstrate that our proposed method outperforms state-of-the-art algorithms in terms of clustering accuracy.

关键词： clustering algorithms Partitioning algorithms Prototypes clustering methods Sparse matrices Noise measurement Power capacitors Dual-sparse framework local subspace possi- bilistic K-subspace (KSS) clustering

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TC-DTW: Accelerating multivariate dynamic time warping through triangle inequality and point clustering

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INFORMATION SCIENCES 2023年 621卷 611-626页

作者： Shen, Daniel S. Chi, Min Stanford Univ 450 Serra Mall Stanford CA 94305 USA North Carolina State Univ 890 Oval Dr Raleigh NC 27695 USA

Dynamic time warping (DTW) plays an important role in analytics on time series. Despite the large body of research on speeding up univariate DTW, the method for multivariate DTW has not been improved much in the last two decades. The most popular algorithm used today is still the one developed nineteen years ago. This paper presents a solution that, as far as we know, for the first time consistently outperforms the classic multivariate DTW algorithm across dataset sizes, series lengths, data dimensions, temporal window sizes, and machines. The new solution, named TC-DTW, introduces triangle inequality and point clustering into the algorithm design on lower bound calculations for multivariate DTW. In experiments on DTW-based nearest neighbor finding, the new solution avoids as much as 98% (60% average) DTW distance calculations and yields as much as 25x (7.5x average) speedups. (C) 2022 Elsevier Inc. All rights reserved.

关键词： clustering algorithms

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Data-Driven clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 5287-5301页

作者： Fontana, Marco Garcia-Fernandez, Angel F. Maskell, Simon Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Univ Antonio de Nebrija ARIES Res Ctr Madrid 28015 Spain

This article proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimization. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and intertrack swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than 1000 targets.

关键词： Target tracking Merging clustering algorithms Time measurement Density measurement Minimization Bayes methods Bayesian estimation multitarget tracking (MTT) Poisson multi-Bernoulli mixtures (PMBM) random finite sets (RFS)

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Geodesic Affinity Propagation clustering Based on Angle-Based Outlier Factor

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IEEE ACCESS 2023年 11卷 43619-43629页

作者： Wang, Chaojie Ju, Jiaqi Beijing Technol & Business Univ Sch Math & Stat Beijing 100048 Peoples R China

The affinity propagation (AP) clustering algorithm has received a lot of attention over the past few years. AP is efficient and insensitive to initialization, and generates clustering results with lower error and in less time. However, there are still two key limitations: AP-related algorithms cannot identify outliers in clusters. And they are usually not ideal for processing nonlinear data. To address the above issues, we propose a geodesic affinity propagation clustering algorithm based on angle-based outlier factor (ABOF-GAP). First, outliers are identified according to the value of angle-based outlier factor. Besides, Euclidean distance is replaced with geodesic distance to measure similarity. Experiments on synthetic data and real data illustrate the effectiveness of the ABOF-GAP algorithm.

关键词： clustering algorithms Euclidean distance Synthetic data Size measurement Measurement uncertainty Anomaly detection Object recognition Affinity propagation (AP) geodesic distances outlier identification angle-based outlier factor (ABOF)

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Enhanced Protein Complex Detection Using Square clustering Coefficient

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Soft Computing 2025年第7期29卷 1-14页

作者： Mirzaee, Parimah Moghaddam Charkari, Nasrollah Roayaei, Mehdy Department of Electrical and Computer Engineering Tarbiat Modares University Tehran14115-111 Iran

Identifying protein complexes from protein-protein interaction networks is one of the crucial tasks in computational biology. Traditional methods, along with their shortcomings in fully understanding protein complex composition, also have inherent limitations and are expensive to implement. In this paper, we introduce a novel method that not only acknowledges but actively tackles these challenges. Our approach, centered around a core-attachment framework, employs a blend of topological metrics, such as square clustering coefficients, in conjunction with traditional clustering coefficients. After establishing the core, we incorporate attachment proteins based on specific conditions employing a depth-first search (DFS) methodology, to form a protein complex. By harnessing multiple metrics, our goal is to elevate the accuracy of protein complex identification beyond what single-metric approaches can achieve. To validate the effectiveness of our approach, we conducted extensive experiments using multiple datasets, including Gavin06, Krogan core, Krogan extend, and DIP datasets, and assessed metrics such as precision, recall, F-measure, and coverage. Our results not only demonstrate the superiority of our method over traditional approaches but also align with findings from related studies. Overall, our study contributes to the ongoing efforts in computational biology by presenting a comprehensive approach to protein complex identification that addresses the shortcomings of previous methods. Through a combination of innovative techniques and insights from recent research, we aim to push the boundaries of accuracy and comprehensiveness in protein complex detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： clustering algorithms

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Neural Network Aided User clustering in mmWave-NOMA Systems With User Decoding Capability Constraints

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IEEE ACCESS 2023年 11卷 45672-45687页

作者： Rajasekaran, Aditya S. S. Yanikomeroglu, Halim Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Ericsson Canada Inc Ottawa ON K2K 2V6 Canada

This paper proposes a computationally efficient two-stage machine learning based approach using neural networks to solve the cluster assignment problem in a millimeter wave-non orthogonal multiple access (mmWave-NOMA) system where each user's individual successive interference cancellation (SIC) decoding capabilities are taken into consideration. The artificial neural network (ANN) is applied in real time to assign users to clusters taking each user's instantaneous channel state information (CSI) and SIC decoding capabilities as inputs. The algorithm is trained offline on cloud resources, i.e., not using the base station (BS) compute resources. This training is done using a dataset obtained by offline computation of input parameters using the optimization algorithms called NOMA-minimum exact cover (NOMA-MEC) and NOMA-best beam (NOMA-BB) from our earlier work in this area. As a result, we term the proposed algorithms in this paper as ANN-NOMA-MEC and ANN-NOMA-BB, respectively. The problem with applying optimization techniques, even low-complexity heuristics, in live networks for user clustering is that they require a very large number of computation steps to make a clustering decision. If these clustering decisions are based on the instantaneous channel of hundreds of users, it becomes prohibitively complex to implement in practical systems on a millisecond granularity as required by beyond 5G (B5G) systems. Instead, our proposed approach takes all this complexity offline and even off the BS compute resources and instead only applies a trained neural network to make such clustering decisions at a microsecond granularity on hundreds of users. Simulation results show the effectiveness of the ANN-NOMA-MEC and ANN-NOMA-BB schemes as the neural network trained on offline simulation data performs comparably with the NOMA-MEC and NOMA-BB heuristics that is applying computationally intensive algorithms to make every clustering decision in a live network.

关键词： Decoding Millimeter wave communication NOMA Complexity theory clustering algorithms Interference cancellation Machine learning Non-orthogonal multiple access (NOMA) successive interference cancellation (SIC) neural networks (NN) millimeter-wave (mmWave) user clustering (UC)

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Computational-Intelligence-Inspired Adaptive Opportunistic clustering Approach for Industrial IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2023年第9期10卷 7884-7892页

作者： Chithaluru, Premkumar Al-Turjman, Fadi Kumar, Manoj Stephan, Thompson Chaitanya Bharathi Inst Technol Dept Comp Sci & Engn Hyderabad 500075 India Univ Kyrenia Fac Engn Res Ctr AI & IoT TR-99320 Kyrenia Turkiye Near East Univ AI & Robot Inst Artificial Intelligence Engn Dept 1403 Nicosia Istanbul Turkiye Univ Wollongong Dubai Fac Engn & Informat Sci Dubai U Arab Emirates M S Ramaiah Univ Appl Sci Dept Comp Sci & Engn Bengaluru 560054 India

The major issues and challenges of the Industrial Internet of Things (IIoT) include network resource management, self-organization;routing, mobility, scalability, security, and data aggregation. Resource management in IIoT is a challenging issue, starting from the deployment and design of sensor nodes, networking at cross-layer, networking software development, application types, environmental conditions, monitoring user decisions, querying process, etc. In this article, computational intelligence (CI) and its computing, such as neural networks and fuzzy logic, are used to tackle the challenges of resource management in the IIoT. The incorporation of the neuro-fuzzy technique into the IIoT contributes to the self-managing intelligence systems' self-organizing and self-sustaining capabilities, offering real-time computations and services in a pervasive networking environment. Most of the problems in IIoT are real-time based;they require fast computation, real-time optimal solutions, and the need to be adaptive to the situation of the events and data traffic to achieve the desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in IIoT networks. A real-time testbed network is simulated and implemented on the Crossbow mote (sensor node) using TinyOS.

关键词： Industrial Internet of Things Peer-to-peer computing Real-time systems Routing Resource management Quality of service clustering algorithms Computational intelligence (CI) industrial IoT mobile node neuro-fuzzy technique resource management self-managing self-organizing self-sustaining

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