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An Optimized clustering Approach for Wireless Sensor Networks Using Improved Squirrel Search Algorithm (ISSA)

IEEE ACCESS 2024年 12卷 134672-134687页

作者： Shalu, S. Berin Sarobin, M. Vergin Raja Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

Wireless Sensor Networks (WSNs) are experiencing rapid growth, reshaping human lifestyles. They consist of numerous spatially distributed sensor nodes dedicated to monitoring and storing environmental data. WSNs have been a vital component of the Internet of Things (IoT) since its inception, where all sensor nodes typically connect to the Internet for data exchange. However, WSNs lack a direct Internet connection for their nodes, necessitating the use of mediators to establish connectivity. Substantial scholarly research in recent years has focused on integrating WSNs into the IoT. WSN consist of a large number of sensors but face constraints due to limited energy capacity. These nodes can perform effectively in challenging environmental conditions, but their batteries cannot be recharged or replaced in harsh environments and also will lead to problems in basis of cost. Consequently, energy preservation is crucial for the network's sustainability. In the field of networking, routing protocols play a significant role in impacting energy consumption. The primary focus during the design of routing protocols is the optimization of energy utilization. This paper aims to minimize overall energy consumption and enhance the network's lifespan using Cluster-based routing algorithms. Clusters, which are essentially groups of sensors, feature a central entity called the Cluster Head (CH), along with associated sensor nodes (SN). CHs are high-energy nodes responsible for collecting and transmitting data from other nodes to the base station (BS). This research introduces a novel energy-efficient clustering approach based on the Nature Inspired Improved Squirrel Search Algorithm (ISSA) with specified objective functions. The selection of CHs considers factors like node residual energy, distance from neighbors and distance from the base station, node degree, and load based on packet processing. Significantly, this research enables energy efficient, scalable and reliable networks.

关键词： Wireless sensor networks Energy efficiency Optimization clustering algorithms Internet of Things Energy consumption Wireless communication Bio-inspired computing energy efficiency clustering CH selection bio inspired optimization IoT

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Multi-View MERA Subspace clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 3102-3112页

作者： Long, Zhen Zhu, Ce Chen, Jie Li, Zihan Ren, Yazhou Liu, Yipeng Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unbalanced unfolding matrices or rotation sensitivity, failing to fully explore inter/intra-view information. Using the advanced tensor network, namely, multi-scale entanglement renormalization ansatz (MERA), we propose a low-rank MERA based MSC (MERA-MSC) algorithm, where MERA factorizes a tensor into contractions of one top core factor and the rest orthogonal/semi-orthogonal factors. Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor. The alternating direction method of multipliers is adopted to solve the optimization model. Experimental results on five multi-view datasets demonstrate MERA-MSC has superiority against the compared algorithms on six evaluation metrics. Furthermore, we extend MERA-MSC by incorporating anchor learning and develop a scalable low-rank MERA based multi-view clustering method (sMREA-MVC). To our knowledge, this is the first work to introduce MERA to the multi-view clustering topic. The effectiveness and efficiency of sMERA-MVC have been validated on three large-scale multi-view datasets.

关键词： Tensors Correlation Matrix decomposition clustering algorithms Task analysis Sparse matrices Singular value decomposition Low-rank tensor approximation MERA decomposition multi-view subspace clustering self-representation learning

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Enhanced Adjacency-Constrained Hierarchical clustering Using Fine-Grained Pseudo Labels

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第3期8卷 2481-2492页

作者： Yang, Jie Lin, Chin-Teng Univ Technol Sydney Australian AI Inst GrapheneX UTS HAI Ctr Computat Intelligence & Brain Comp Interface LabS Sydney NSW 2007 Australia

Hierarchical clustering is able to provide partitions of different granularity levels. However, most existing hierarchical clustering techniques perform clustering in the original feature space of the data, which may suffer from overlap, sparseness, or other undesirable characteristics, resulting in noncompetitive performance. In the field of deep clustering, learning representations using pseudo labels has recently become a research hotspot. Yet most existing approaches employ coarse-grained pseudo labels, which may contain noise or incorrect labels. Hence, the learned feature space does not produce a competitive model. In this paper, we introduce the idea of fine-grained labels of supervised learning into unsupervised clustering, giving rise to the enhanced adjacency-constrained hierarchical clustering (ECHC) model. The full framework comprises four steps. One, adjacency-constrained hierarchical clustering (CHC) is used to produce relatively pure fine-grained pseudo labels. Two, those fine-grained pseudo labels are used to train a shallow multilayer perceptron to generate good representations. Three, the corresponding representation of each sample in the learned space is used to construct a similarity matrix. Four, CHC is used to generate the final partition based on the similarity matrix. The experimental results show that the proposed ECHC framework not only outperforms 14 shallow clustering methods on eight real-world datasets but also surpasses current state-of-the-art deep clustering models on six real-world datasets. In addition, on five real-world datasets, ECHC achieves comparable results to supervised algorithms.

关键词： clustering algorithms Merging Partitioning algorithms Training Supervised learning Corporate acquisitions Computational intelligence clustering hierarchical clustering fine-grained pseudo labels representation learning

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Mahalanobis-Kernel Distance-Based Suppressed Possibilistic C-Means clustering Algorithm for Imbalanced Image Segmentation

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第8期32卷 4595-4609页

作者： Yu, Haiyan Xie, Shuang Fan, Jiulun Lan, Rong Lei, Bo Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian 710121 Peoples R China Xian Key Lab Image Proc Technol & Applicat Publ Se Xian 710121 Peoples R China

The possibilistic c-means (PCM) clustering is an important unsupervised pattern recognition method. However, it is still faced with huge challenges in clustering multidimensional data with multiple characteristics, such as imbalanced sample sizes, imbalanced feature components, noise and outlier corruption, and the sparse distribution of small targets in the feature space caused by the "curse of dimensionality." In view of this, this article proposes a PCM clustering algorithm based on the Mahalanobis-Kernel Distance and the suppressed competitive learning strategy. To begin with, the Mahalanobis-Kernel Distance combined with the absolute attribute of possibilistic memberships is proposed to enhance the intra-class compactness of small targets with sparse distribution and feature imbalance. In addition, to overcome the inherent coincident clustering problem caused by possibilistic memberships, the "suppressed competitive learning" mechanism based on the Mahalanobis-Kernel distance is designed to generate cluster cores and correct memberships of objects located within the cluster cores, thus guiding purposefully the clustering process. Furthermore, spatial information is introduced by the membership filtering scheme to improve the segmentation effect of color images with small targets and noise injection. Experimental results show that the algorithm in this article can achieve better clustering and segmentation performance than several state-of-the-art fuzzy clustering methods for color images with imbalanced sizes and features and noise injection.

关键词： clustering algorithms Phase change materials Partitioning algorithms Noise Competitive learning Image segmentation Kernel clustering image segmentation imbalanced data kernel-based clustering Mahalanobis distance possibilistic c-means clustering (PCM)

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Feature selection with clustering probabilistic particle swarm optimization

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第9期15卷 3599-3617页

作者： Gao, Jinrui Wang, Ziqian Lei, Zhenyu Wang, Rong-Long Wu, Zhengwei Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Toyama Univ Gufuku 3190 Toyama Toyama 9308555 Japan Univ Fukui Fac Engn Fukui 9108507 Japan Univ Fukui 3-9-1 Bunkyo Fukui Fukui 9108507 Japan Tongji Univ State Key Lab Marine Geol Shanghai 200092 Peoples R China Tongji Univ 1239 Siping Rd Shanghai 200092 Jiangsu Peoples R China

Dealing with high-dimensional data poses a significant challenge in machine learning. To address this issue, researchers have proposed feature selection as a viable solution. Due to the intricate search space involved in feature selection, swarm intelligence algorithms have gained popularity for their exceptional search capabilities. This study introduces a method called clustering Probabilistic Particle Swarm Optimization (CPPSO) to revolutionize the traditional particle swarm optimization approach by incorporating probabilities to represent velocity and incorporating an elitism mechanism. Furthermore, CPPSO employs a clustering strategy based on the K-means algorithm, utilizing the Hamming distance to divide the population into two sub-populations to improve the performance. To assess CPPSO's performance, a comparative analysis is conducted against seven existing algorithms using twenty diverse datasets. These datasets are all based on real-world problems. Fifteen of these are frequently used in feature selection research, while the remaining five comprise imbalanced datasets as well as multi-label datasets. The experimental results demonstrate the superiority of CPPSO across a range of evaluation criteria, surpassing the performance of the comparative algorithms on the majority of the datasets.

关键词： Feature selection Particle swarm optimization Classification clustering algorithms

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Adaptive clustering With Similarity Learning for Enhanced Multi-Scenario Radar Signal Processing

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1840-1844页

作者： Sun, Qi Cao, Zhen Huang, Yulei Ge, Yifan Wang, Yunlong Xidian Univ Sch Artificial Intelligence Xian 710126 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China

In the complex and dynamic electromagnetic environment of modern electronic reconnaissance systems, high-density and highly intertleaving signals cause challenges to radar signal sorting (RSS). This paper introduces a novel RSS method based on similarity learning and pseudo-labeling to address the pulse deinterleaving problem in complex scenarios. The method is divided into two stages: first, unsupervised clustering is used to generate pseudo-labels necessary for calculating the pulse repetition interval (PRI);then, a two-branch network based on similarity learning uses the PRI and other signal features to determine whether two clusters correspond to the same radar. By combining pseudo-labeling with similarity learning, the method improves the performance in high-density and highly interleaving environments, provides better robustness and calculation time, and thus effectively meets the requirements of current electronic reconnaissance tasks.

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A Hybrid Multi-Hop clustering and Energy-Aware Routing Protocol for Efficient Resource Management in Renewable Energy Harvesting Wireless Sensor Networks

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IEEE ACCESS 2024年 12卷 137310-137332页

作者： Jalalinejad, Hoda Rohani Hajiabadi, Mahdi Asghar Rahmani Hosseinabadi, Ali Mirkamali, Seyedsaeid Abraham, Ajith Weber, Gerhard-Wilhelm Parikh, Jinal Islamic Azad Univ Dept Math & Comp Sci Bandar Abbas Branch Bandar Abbas *** Iran Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin *** Iran Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada Payame Noor Univ Dept Comp Engn & IT Tehran 193954697 Iran Bennett Univ Sch Artificial Intelligence Greater Noida 201310 Uttar Pradesh India Innopolis Univ Ctr Artificial Intelligence Innopolis 420500 Russia Poznan Univ Tech Fac Engn & Management PL-60965 Poznan Poland Ahmedabad Univ Amrut Mody Sch Management Technol Operat & Decis Sci Ahmadabad 380009 India

Energy Harvesting Wireless Sensor Networks (EH-WSNs) main goal is to increase efficiency in settings where Energy Harvesting (EH) is restricted by environmental resources. To solve the drawbacks of conventional Wireless Sensor Networks (WSNs) routing methods that usually ignore EH, this work presents a multi-hop clustering and renewable energy-based routing protocol designed for EH-WSNs. The suggested method performs clustering both centralized and decentralized using energy circumstances and the quantity of captured energy. The protocol operates in three phases: cluster formation, data transmission, and centralized management. To evaluate the effectiveness of the proposed approach, we analyze three distinct scenarios with different settings. The findings show that the suggested approach greatly lowers the total network energy usage while allowing a higher number of nodes to stay operational. We find that our approach outperforms AEHAC, CRBS, HUCL, and EADUC in terms of average energy levels, overall efficiency, network stability, and number of live nodes during the simulation. The results taken together show that the suggested method continuously improves network efficiency and stability in all assessed situations.

关键词： Wireless sensor networks Sensors clustering algorithms Routing Batteries Routing protocols Renewable energy sources Energy harvesting Distributed computing clustering protocol energy harvesting energy-aware distributed computing

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Accurate Complementarity Learning for Graph-Based Multiview clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 16106-16118页

作者： Xiao, Xiaolin Gong, Yue-Jiao South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China South China Normal Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 610056 Peoples R China

In real scenarios, graph-based multiview clustering has clearly shown popularity owing to the high efficiency in fusing the information from multiple views. Practically, the multiview graphs offer both consistent and inconsistent cues as they usually come from heterogeneous sources. Previous methods illustrated the importance of leveraging the multiview consistency and inconsistency for accurate modeling. However, when fusing the graphs, the inconsistent parts are generally ignored and hence the valued view-specific attributes are lost. To solve this problem, we propose an accurate complementarity learning (ACL) model for graph-based multiview clustering. ACL clearly distinguishes the consistent, complementary, and noise and corruption terms from the initial multiview graphs. In contrast to existing models that overlooked the complementary information, we argue that the view-specific characteristics extracted from the complementary terms are beneficial for affinity learning. In addition, ACL exploits only the positive parts of the complementary information for preserving the evidence on the positive sample relationship, and ignores the negative cues to avoid the vanishing of effective affinity strengths. This way, the learned affinity matrix is able to properly balance the consistent and complementary information. To solve the ACL model, we introduce an efficient alternating optimization algorithm with a varying penalty parameter. Experiments on synthetic and real-world databases clearly demonstrated the superiority of ACL.

关键词： Kernel clustering algorithms Feature extraction Task analysis Faces Computational modeling Computational efficiency Complementarity learning consistency graph fusion graph-based multiview clustering inconsistency

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VMCFDGS: Variable Multicenter Aggregation clustering Method Based on Fuzzy Dominating (Dominated)-Granularity Structure

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第12期32卷 6633-6645页

作者： Yu, Bin Xu, Ruihui Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China

With the continuous surge in data, the order information increases while the distinguishability of the data diminishes. To address the decreased efficiency and stability of traditional clustering methods owing to intercluster overlap and noise, this article proposes a novel method-a variable multicenter aggregation clustering algorithm based on fuzzy dominating (dominated)-granularity structure (VMCFDGS). First, the order characteristics of object attributes are utilized to form a fuzzy dominating (dominated)-granularity structure (FDDGS). Then, we employ the characteristics of the FDDGS to illustrate relations among objects from various perspectives. Furthermore, an approach is proposed that uses a multicenter technique for better description of similarities within and across clusters, clustering data from diverse granularities, and mining clustering outcomes at different levels. Finally, fusion clustering of information purification is achieved based on the optimum results of different granularity clustering on each metric. Comparative results with advanced clustering algorithms on the UCI dataset demonstrate this proposed method's superiority in processing complex spatial structure data and its effectiveness and robustness in handling overlapping and noisy data.

关键词： clustering algorithms clustering methods Robustness Noise Kernel Feature extraction Fuzzy systems clustering fuzzy dominating (dominated)-granularity structure (FDDGS) multicenter aggregation of variables stability variable multicenter aggregation clustering algorithm based on fuzzy dominating (dominated)-granularity structure (VMCFDGS)

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Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View clustering

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IEEE ACCESS 2024年 12卷 85806-85821页

作者： Jamil, Saadia Rehman, Eid Shahzad, Tariq Ishtiaq, Muhammad Mazhar, Tehseen Yasin Ghadi, Yazeed Ahmed, Arfan Fdn Univ Islamabad Dept Software Engn Islamabad 44000 Pakistan Univ Mianwali Dept Comp Sci & Informat Technol Mianwali 42200 Punjab Pakistan COMSATS Univ Islamabad Dept Comp Sci Sahiwal Campus Sahiwal 57000 Pakistan Natl Univ Comp & Emerging Sci Dept Data Sci & Artificial Intelligence Islamabad 44000 Pakistan Virtual Univ Pakistan Dept Comp Sci Lahore 55150 Pakistan Al Ain Univ Dept Comp Sci & Software Engn Abu Dhabi U Arab Emirates Weill Cornell Med Qatar AI Ctr Precis Hlth Doha Qatar

clustering has proved to be a successful classification method when it comes to dealing with multiview data. Each method and technique tries to achieve efficiency and accuracy in classifying the multiview data. Multi-source data contains noise and divergence. Another problem is that each view contains many features, so usually the multiview dataset is multi-dimensional. This raises basic problems like the need for a dimensionality reduction technique for optimal selection of features, fusing the data of different views, and maintaining the inter- and intra-consensus of the multiview dataset. The fusion technique should merge the complementary information efficiently. The goal of this study is to use a promising technique for dimension reduction that reduces the noise but maintains the inter-view and cross-view consensus. A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. It calculates the dimension relevance with various data instances. These further place the relevant dimension samples in one group. The method does not need to know the number of clusters before hand, as it dynamically determines it during the process. Finally, this study proposed a novel multi-view learning framework that analyzes multi-source data and generates fine clusters efficiently.

关键词： clustering methods Noise measurement clustering algorithms Computer science Self-organizing feature maps Dimensionality reduction Classification algorithms Self organizing map multiview clustering classification pipeline

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