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Projective Multiple Kernel Subspace clustering

IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 2567-2579页

作者： Sun, Mengjing Wang, Siwei Zhang, Pei Liu, Xinwang Guo, Xifeng Zhou, Sihang Zhu, En Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Multiple kernel subspace clustering (MKSC), as an important extension for handling multi-view non-linear subspace data, has shown notable success in a wide variety of machine learning tasks. The key objective of MKSC is to build a flexible and appropriate graph for clustering from the kernel space. However, existing MKSC methods apply a mechanism utilizing the kernel trick to the traditional self-expressive principle, where the similarity graphs are built on the respective high-dimensional (or even infinite) reproducing kernel Hilbert space (RKHS). Regarding this strategy, we argue that the original high-dimensional spaces usually include noise and unreliable similarity measures and, therefore, output a low-quality graph matrix, which degrades clustering performance. In this paper, inspired by projective clustering, we propose the utilization of a complementary similarity graph by fusing the multiple kernel graphs constructed in the low-dimensional partition space, termed projective multiple kernel subspace clustering (PMKSC). By incorporating intrinsic structures with multi-view data, PMKSC alleviates the noise and redundancy in the original kernel space and obtains high-quality similarity to uncover the underlying clustering structures. Furthermore, we design a three-step alternate algorithm with proven convergence to solve the proposed optimization problem. The experimental results on ten multiple kernel benchmark datasets validate the effectiveness of our proposed PMKSC, compared to the state-of-the-art multiple kernel and kernel subspace clustering methods, by a large margin. Our code is available at https://***/MengjingSun/PMKSC-code.

关键词： Kernel Optimization clustering algorithms Integrated circuits Hilbert space Redundancy clustering methods Kernel clustering multiple kernel subspace clustering multi-view information fusion

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Variable-Length Subsequence clustering in Time Series

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第2期34卷 983-995页

作者： Duan, Jiangyong Guo, Lili Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing 100094 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China

Subsequence clustering is an important issue in time series data mining. Observing that most time series consist of various patterns with different unknown lengths, we propose an optimization framework to adaptively estimate the lengths and representations for different patterns. Our framework minimizes the inner subsequence cluster errors with respect to subsequence clusters and segmentation under time series cover constraint where the subsequence cluster lengths can be variable. To optimize our framework, we first generate abundant initial subsequence clusters with different lengths. Then, three cluster operations, i.e., cluster splitting, combination and removing, are used to iteratively refine the cluster lengths and representations by respectively splitting clusters consisting of different patterns, joining neighboring clusters belonging to the same pattern and removing clusters to the predefined cluster number. During each cluster refinement, we employ an efficient algorithm to alternatively optimize subsequence clusters and segmentation based on dynamic programming. Our method can automatically and efficiently extract the unknown variable-length subsequence clusters in the time series. Comparative results with the state-of-the-art are conducted on various synthetic and real time series, and quantitative and qualitative performances demonstrate the effectiveness of our method.

关键词： Time series analysis Data mining Optimization clustering algorithms clustering methods Adaptation models Feature extraction Time series data mining subsequence clustering variable-length patterns time series segmentation

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Multiobjective Sine Cosine Algorithm for Remote Sensing Image Spatial-Spectral clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第10期52卷 11172-11186页

作者： Wan, Yuting Ma, Ailong Zhang, Liangpei Zhong, Yanfei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Hubei Prov Engn Res Ctr Nat Resources Remote Sens Wuhan 430079 Peoples R China

Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing image data spatial-spectral clustering (MOSCA_SSC) is proposed. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing image datasets.

关键词： Optimization Remote sensing clustering algorithms Linear programming Indexes Task analysis Genetic algorithms Image data clustering knee point-based selection multiobjective optimization remote sensing sine cosine algorithm (SCA) spatial information

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CTB-PKI: clustering and Trust Enabled Blockchain Based PKI System for Efficient Communication in P2P Network

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IEEE ACCESS 2022年 10卷 124277-124290页

作者： Panigrahi, Amrutanshu Nayak, Ajit Kumar Paul, Rourab Sahu, Bibhuprasad Kant, Shashi Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Deemed Univ Dept Comp Sci & Informat Technol Bhubaneswar 751030 Odisha India Vardhaman Coll Engn Autonomous Dept AI & DS Hyderabad 501218 Telangana India Bule Hora Univ Coll Business & Econ Dept Management Oromia Ethiopia

The decentralization feature of public and private blockchain-based applications is achieved by selecting different nodes as validators or Certificate Authority (CA) for each transaction. Public blockchain uses Proof of Work (PoW) to search for the validator. PoW causes an enormous amount of energy. Therefore, Proof of Stake (PoS), and Proof of Authority (PoA) emerged as alternate solutions. Selection of a new CA using PoS or PoA algorithms for each transaction may improve transaction security. However, a network may have a large number of transactions and participants. Selecting a CA for each transaction using PoS or PoA may cause a significant amount of block propagation delay, which can reduce network efficiency drastically. This paper proposes a different approach to increase the efficiency of Blockchain-based Public Key Infrastructure (BC - PKI ). The proposed approach creates clusters of participant nodes based on their validation time, response time, and trust. This method selects a cluster based on the budget of response time and validation time given by the node that intends to start a transaction. Thereafter, the node which has the highest trust in that cluster is chosen as a CA for the next transaction. Instead of searching on all participant nodes, our approach searches on the nodes of the chosen cluster which reduces the searching space of the CA selection process. This research work adopts a trust evaluation approach where the trust factor is quantified based on its experience and reputation. The node trust is reevaluated after every successful and unsuccessful transaction. A node that performs more successful transactions has more trust value. The node that has a higher trust value has a higher probability to be selected as a CA for a transaction. The trust reevaluation process is followed by the clustering process. The result shows the proposed approach can reduce similar to 38.5% response time and similar to 2.2% validation time as compared to infra

关键词： Peer-to-peer computing Blockchains Protocols Time factors Servers Evidence theory clustering algorithms Smart contracts Blockchain certificate authority clustering public key infrastructure smart contract

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Deep Feature-Based Text clustering and its Explanation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3669-3680页

作者： Guan, Renchu Zhang, Hao Liang, Yanchun Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Jilin Univ Coll Comp Sci & Technol Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Zhuhai Coll Key Lab Symbol Computat & Knowledge Engn Zhuhai LabMinist Educ Zhuhai 519041 Peoples R China Univ Trento DISI I-38122 Trento Italy

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms and the state-of-the-art pretrained language model, i.e., BERT, on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results.

关键词： Task analysis Computational modeling Feature extraction clustering algorithms Semantics Data models Recurrent neural networks Deep learning explanation model feature extraction text clustering transfer learning

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Tsbagging: A Novel Cross-Project Software Defect Prediction Algorithm Based on Semisupervised clustering

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SCIENTIFIC PROGRAMMING 2022年第0期2022卷

作者： Tang, Shiqi Huang, Song Liu, ErHu Yao, YongMing Wu, KaiShun Ji, Haijin Army Engn Univ PLA Command & Control Engn Coll Nanjing 210007 Jiangsu Peoples R China Huaiyin Normal Univ Sch Comp Sci & Technol Huaian Jiangsu Peoples R China

Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages;in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods.

关键词： clustering algorithms

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Hierarchical clustering Nodes Collaborative Scheduling in Wireless Sensor Network

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IEEE SENSORS JOURNAL 2022年第2期22卷 1786-1798页

作者： Wang, Minghua Zeng, Jingxian Univ South China Sch Elect Engn Hunan Prov Key Lab Ultrafast Micro Nano Technol & Hengyang 421001 Peoples R China

Network lifetime and coverage are the key evaluation metrics of Wireless Sensor Networks (WSNs). Aiming at network lifetime, Hierarchical clustering-task Scheduling Policy (HCSP) breaks the traditional clustering framework, but it doesnt fully consider the network coverage problem. We propose a Hierarchical clustering Node Collaborative Scheduling (HCNCS) protocol that optimizes HCSP to extend the network lifetime and improves the coverage performance of WSNs by nodes collaborative sensing. According to the deployment location, HCNCS classifies nodes into Node Collaborative Sensing Disks(NCSDs), which as units for data sensing and transmission. Nodes are rotated in WSNs to balance the energy consumption and through collaborative sensing to improve the coverage of NCSDs. In addition, HCNCS constrains the number of clusters by NCSDs classification based on K-means to reduce the communication energy consumption of clustering. Based on the HCNCS, this paper further proposes an Enhanced Hierarchical clustering Node Collaborative Scheduling (EHCNCS) protocol. Compared with HCNCS, EHCNCS selects NCSDs according to Node Collaborative Scheduling (NCS) algorithm after clustering, which improves the coverage capability of NCSDs at the cost of increasing a small amount of the communication energy consumption. EHCNCS also rotates Cluster Heads (CHs) by the CH rotation candidate table to balance network energy consumption. The extensive simulations show that HCNCS has an obvious advantage over HCSP in terms of network lifetime and network coverage. Compared with HCNCS, EHCNCS significantly prolongs the network lifetime of WSNs at the expense of a bit of coverage performance.

关键词： Wireless sensor networks Sensors Collaboration Protocols Energy consumption clustering algorithms Routing Wireless sensor networks hierarchical clustering-task scheduling policy K-means nodes collaborative sensing

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Supervertex Sampling Network: A Geodesic Differential SLIC Approach for 3D Mesh

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第8期30卷 5553-5565页

作者： Zhuang, Jiafu Zeng, Pan Zhuang, Wei Guo, Xiaoyu Liu, Peizhong Quanzhou Normal Univ Sch Phys & Informat Engn Quanzhou 362000 Fujian Peoples R China Huaqiao Univ Sch Med Quanzhou 362021 Fujian Peoples R China Univ Manchester Dept Social Stat Manchester M13 9PL England Quanzhou Normal Univ Coll Oceanol & Food Sci Quanzhou 362000 Fujian Peoples R China Huaqiao Univ Coll Engn Quanzhou 362021 Fujian Peoples R China Quanzhou Med Coll Sch Med Quanzhou 362021 Fujian Peoples R China

The analysis of 3D meshes with deep learning has become prevalent in computer graphics. As an essential structure, hierarchical representation is critical for mesh pooling in multiscale analysis. Existing clustering-based mesh hierarchy construction methods involve nonlinear discretization optimization operations, making them nondifferential and challenging to embed in other trainable networks for learning. Inspired by deep superpixel learning methods in image processing, we extend them from 2D images to 3D meshes by proposing a novel differentiable chart-based segmentation method named geodesic differential supervertex (GDSV). The key to the GDSV method is to ensure that the geodesic position updates are differentiable while satisfying the constraint that the renewed supervertices lie on the manifold surface. To this end, in addition to using the differential SLIC clustering algorithm to update the nonpositional features of the supervertices, a reparameterization trick, the Gumbel-Softmax trick, is employed to renew the geodesic positions of the supervertices. Therefore, the geodesic position update problem is converted into a linear matrix multiplication issue. The GDSV method can be an independent module for chart-based segmentation tasks. Meanwhile, it can be combined with the front-end feature learning network and the back-end task-specific network as a plug-in-plug-out module for training;and be applied to tasks such as shape classification, part segmentation, and 3D scene understanding. Experimental results show the excellent performance of our proposed algorithm on a range of datasets.

关键词： Three-dimensional displays clustering algorithms Shape Task analysis Manifolds Deep learning Training 3D mesh segmentation 3D shape learning deep clustering graph neural network

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An Unequal clustering Method Based on Particle Swarm Optimization in Underwater Acoustic Sensor Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第24期9卷 25027-25036页

作者： Hou, Rui Fu, Juan Dong, Mianxiong Ota, Kaoru Zeng, Deze South Cent Minzu Univ Coll Comp Sci Wuhan 430074 Hubei Peoples R China Muroran Inst Technol Dept Informat & Elect Engn Muroran 0508585 Japan China Univ Geosci Sch Comp Sci Wuhan 430079 Peoples R China

Underwater acoustic sensor networks (UASNs) currently provide an important technical means of underwater communication, but there are difficulties in power updating or power replenishment because the sensor nodes work in an underwater environment. Therefore, energy consumption optimization has become the focus of research on UASNs. Node clustering is widely considered to be able to optimize network energy consumption. Although the current clustering-based routing method prolongs the network life cycle to a certain extent, some nodes may fail due to excessive energy consumption caused by excessive data transmission tasks, and the problems of high and uneven energy consumption still exist. To extend the life cycle of UASNs, this article proposes an unequal clustering method based on particle swarm optimization. Our method uses iterative updates to dynamically adjust the cluster size based on the remaining energy of the cluster head, the distance from the cluster head to the Sink node, and the number of times the cluster head forwards data between clusters to balance the cluster head load. The energy consumption of the whole path from the cluster head to the Sink node and the number of hops required are considered in the intercluster transmission phase. Simulation results show that this method can effectively reduce the network energy consumption.

关键词： Energy consumption clustering algorithms Optimization clustering methods Routing Underwater acoustics Internet of Things clustering energy consumption optimization underwater acoustic sensor networks (UASNs)

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A self-adaptive particle swarm optimization based K-means (SAPSO-K) clustering method to evaluate fabric tactile comfort

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JOURNAL OF THE TEXTILE INSTITUTE 2022年第5期113卷 915-926页

作者： Sun, Yi Liu, Gui Zheng, Dongming Zou, Haochen Liu, Zhenrui Liu, Jinkang Du, Zhaoqun Donghua Univ Minist Educ Engn Res Ctr Tech Text Shanghai Peoples R China Donghua Univ Coll Text Shanghai Peoples R China Fujian Fiber Inspect Ctr Fujian Prov Key Lab Text Inspect Technol Fuzhou Peoples R China Jiangxi Prov Ctr Qual Inspect & Supervis Prod Gongqingcheng Peoples R China Violet Home Text Sci & Technol Co Ltd Nantong Peoples R China

This article proposes a new clustering algorithm of the self-adaptive particle swarm optimization (PSO) based K-means (SAPSO-K) to evaluate the fabric tactile comfort and compared with other algorithms. In this study, the efficient characterization of Quick-Intelligent Handle Evaluation System (QIHES) method was used to detect the mechanical properties, where 10 feature indexes were extracted from the testing force-displacement curves. The correlation analysis was used to determine the appropriate clustering data set and the different number of groups was applied to determine the optimal cluster number. Moreover, subjective evaluation was conducted and compared with the conventional clustering algorithms of K-means, agglomerative hierarchical clustering (AHC), PSO-based K-means(PSO-K) and SAPSO-K. The results show that the clustering results based on 10 indexes are significantly better than the eight indexes with good correlation, which indicate the performance of the stretching stage has a certain degree of influence on the fabric tactile comfort. The PSO-K and SAPSO-K algorithms have the minimum average relative error compared to the subjective rating values (RV) as 4.455 with the optimal cluster number as 5. Nevertheless, SAPSO-K achieved the best fitness of 6.192 after eight iterations, while PSO-K needs 136 times. Therefore, the SAPSO-K clustering algorithm is the most effective and reliable method to predict the RV and evaluate the tactile comfort with good accuracy.

关键词： Tactile comfort SAPSO-K QIHES clustering algorithms PSO-K

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