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Regularized Simple Multiple Kernel k-Means With Kernel Average Alignment

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15910-15919页

作者： Li, Miaomiao Zhang, Yi Ma, Chuan Liu, Suyuan Liu, Zhe Yin, Jianping Liu, Xinwang Liao, Qing Changsha Univ Coll Elect Informat & Elect Engn Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 211189 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Dongguan Univ Technol Dongguan 523820 Guangdong Peoples R China Harbin Inst Technol Dept Comp Sci & Technol Shenzhen 518055 Peoples R China

Multiple kernel clustering (MKC) aims to learn an optimal kernel to better serve for clustering from several precomputed basic kernels. Most MKC algorithms adhere to a common assumption that an optimal kernel is linearly combined by basic kernels. Based on a min-max framework, a newly proposed MKC method termed simple multiple kernel k-means (SimpleMKKM) can acquire a high-quality unified kernel. Although SimpleMKKM has achieved promising clustering performance, we observe that it cannot benefit from any prior knowledge. This would cause the learned partition matrix may seriously deviate from the expected one, especially in clustering tasks where the ground truth is absent during the learning course. To tackle this issue, we propose a novel algorithm termed regularized simple multiple kernel k-means with kernel average alignment (R-SMKKM-KAA). According to the experimental results of existing MKC algorithms, the average partition is a strong baseline to reflect true clustering. To gain knowledge from the average partition, we add the average alignment as a regularization term to prevent the learned unified partition from being far from the average partition. After that, we have designed an efficient solving algorithm to optimize the new resulting problem. In this way, both the incorporated prior knowledge and the combination of basic kernels are helpful to learn better unified partition. Consequently, the clustering performance can be significantly improved. Extensive experiments on nine common datasets have sufficiently demonstrated the effectiveness of incorporation of prior knowledge into SimpleMKKM.

关键词： Kernel Optimization Partitioning algorithms clustering algorithms Tensors Minimization Learning systems Multiple kernel clustering (MKC) multiple kernel learning

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Rapid determination of lambda-cyhalothrin residues on Chinese cabbage based on MIR spectroscopy and a Gustafson-Kessel noise clustering algorithm

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RSC ADVANCES 2022年第29期12卷 18457-18465页

作者： Zheng, Jun Gong, Zhe Yin, Shaojie Wang, Wei Wang, Meng Lin, Peng Zhou, Haoxiang Yang, Yangjian Zhejiang Univ Taizhou Dept Elect & Control Engn Res Inst Taizhou 318000 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212000 Jiangsu Peoples R China

Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health. In order to quickly, non-destructively and effectively qualitatively analyze lambda-cyhalothrin residues on Chinese cabbage, a method involving a Gustafson-Kessel noise clustering (GKNC) algorithm was proposed to cluster the mid-infrared (MIR) spectra. A total of 120 Chinese cabbage samples with three different lambda-cyhalothrin residue levels (no lambda-cyhalothrin, and cases where the ratios of lambda-cyhalothrin and water were 1 : 500 and 1 : 100) were scanned using an Agilent Cary 630 FTIR spectrometer for collecting the MIR spectra. Next, multiple scatter correction (MSC) was employed to eliminate the effects of light scattering. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) were utilized to reduce the dimensionality and extract the feature information from the MIR spectra. Finally, fuzzy c-means (FCM) clustering, Gustafson-Kessel (GK) clustering, noise clustering (NC) and the GKNC algorithm were applied to cluster the MIR spectral data, respectively. The experimental results showed that the GKNC algorithm gave the best classification performance compared against the other three fuzzy clustering algorithms, and its highest clustering accuracy reached 93.3%. Therefore, the GKNC algorithm coupled with MIR spectroscopy is an effective method for detecting lambda-cyhalothrin residues on Chinese cabbage.

关键词： clustering algorithms

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A Route clustering and Search Heuristic for Large-Scale Multidepot-Capacitated Arc Routing Problem

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第8期52卷 8286-8299页

作者： Zhang, Yuzhou Mei, Yi Huang, Shihua Zheng, Xin Zhang, Cuijuan Anqing Normal Univ Sch Comp & Informat Anqing 246133 Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand

The capacitated arc routing problem (CARP) has attracted much attention for its many practical applications. The large-scale multidepot CARP (LSMDCARP) is an important CARP variant, which is very challenging due to its vast search space. To solve LSMDCARP, we propose an iterative improvement heuristic, called route clustering and search heuristic (RoCaSH). In each iteration, it first (re)decomposes the original LSMDCARP into a set of smaller single-depot CARP subproblems using route cutting off and clustering techniques. Then, it solves each subproblem using the effective Ulusoy's split operator and local search. On one hand, the route clustering helps the search for each subproblem by focusing more on the promising areas. On the other hand, the subproblem solving provides better routes for the subsequent route cutting off and clustering, leading to better problem decomposition. The proposed RoCaSH was compared with the state-of-the-art MDCARP algorithms on a range of MDCARP instances, including different problem sizes. The experimental results showed that RoCaSH significantly outperformed the state-of-the-art algorithms, especially for the large-scale instances. It managed to achieve much better solutions within a much shorter computational time.

关键词： Task analysis Routing Optimization clustering algorithms Mathematical model Search problems Scalability Capacitated arc routing problem (CARP) multidepot route clustering scalability search heuristic

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A Deep clustering via Automatic Feature Embedded Learning for Human Activity Recognition

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第1期32卷 210-223页

作者： Wang, Ting Ng, Wing W. Y. Li, Jinde Wu, Qiuxia Zhang, Shuai Nugent, Chris Shewell, Colin South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510641 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510641 Peoples R China Ulster Univ Sch Comp Jordanstown BT37 0QB North Ireland

Traditional clustering algorithms are widely used for building bag-of-words (BOW) models to aggregate spatio-temporal feature points extracted from a video for human activity recognition problems. Their performances are restricted by the computational complexity which limits the number of feature points being used. In contrast, deep clustering yields good clustering performance without the limit of the number of feature points. Therefore, this work proposes a dual stacked autoencoders features embedded clustering (DSAFEC) and a BOW construction method based on the DSAFEC (B-DSAFEC) to reduce the computational complexity and to remove the selection restriction. The DSAFEC first transforms feature points extracted from a video to a learned feature space and then probabilities of cluster assignment of feature points are predicted to build BOWs for human activity recognition. A soft clustering is used by assigning each feature point to multiple clusters yielding the largest probabilities instead of only one in hard clustering. Experimental results on three benchmark human activity datasets show that the B-DSAFEC yields better performance compared to five reference methods which are developed based on either traditional clustering methods or deep clustering methods.

关键词： Feature extraction clustering algorithms clustering methods Visualization Activity recognition Vocabulary Task analysis Bag-of-words (BOW) deep clustering human activity recognition autoencoder

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Tensor Completion-Based Incomplete Multiview clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13635-13644页

作者： Xia, Wei Gao, Quanxue Wang, Qianqian Gao, Xinbo Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Incomplete multiview clustering is a challenging problem in the domain of unsupervised learning. However, the existing incomplete multiview clustering methods only consider the similarity structure of intraview while neglecting the similarity structure of interview. Thus, they cannot take advantage of both the complementary information and spatial structure embedded in similarity matrices of different views. To this end, we complete the incomplete graph with missing data referring to tensor complete and present a novel and effective model to handel the incomplete multiview clustering task. To be specific, we consider the similarity of the interview graphs via the tensor Schatten p-norm-based completion technique to make use of both the complementary information and spatial structure. Meanwhile, we employ the connectivity constraint for similarity matrices of different views such that the connected components approximately represent clusters. Thus, the learned entire graph not only has the low-rank structure but also well characterizes the relationship between unmissing data. Extensive experiments show the promising performance of the proposed method comparing with several incomplete multiview approaches in the clustering tasks.

关键词： Tensors Interviews Task analysis Kernel Optimization clustering methods clustering algorithms Incomplete data multiview clustering tensor completion

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Explainability of Text clustering Visualizations- Twitter Disinformation Case Study

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2022年第4期42卷 8-19页

作者： Zarsky, Jiri Lopez, Gaetan Kliegr, Tomas VSE Praha Prague 13067 Czech Republic

While text clustering methods have been available for decades, there is a paucity of material that would help practitioners with the choice and configuration of suitable algorithms and visualizations. In this article, we present a case study analyzing two disinformation datasets composed of tweets from the era of the 2016 United States Presidential Election. We use this to demonstrate steps for selecting the best configuration of the clustering algorithm and consequently conduct a user experiment for evaluating the comprehensibility of three alternate visualizations. A supplementary GitHub repository contains source code with examples.

关键词： Visualization Adaptation Models Machine Learning algorithms clustering Methods Social Networking Online clustering algorithms Media

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A Novel Modeling Framework Based on Customized Kernel-Based Fuzzy C-Means clustering in Iron Ore Sintering Process

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第2期27卷 950-961页

作者： Hu, Jie Wu, Min Chen, Luefeng Pedrycz, Witold China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada

This article proposes a customized kernel-based fuzzy C-means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based fuzzy C-means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and the Spearman's rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy.

关键词： Data models clustering algorithms Predictive models Mechatronics Iron IEEE transactions Input variables Actual run data broad learning model carbon efficiency prediction customized kernel-based fuzzy C-means (CKFCM) iron ore sintering process (IOSP) multiple operating conditions

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Energy-Saving clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy Inference

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IEEE SENSORS JOURNAL 2022年第3期22卷 2845-2857页

作者： Hou, Jun Qiao, Jianhua Han, Xinglong Taiyuan Univ Sci & Technol Elect Informat Engn Dept Taiyuan 030024 Peoples R China

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, clustering Type1 and clustering Type2, and alternately executes them in different rounds according to the threshold. clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others.

关键词： clustering algorithms Heuristic algorithms Inference algorithms Wireless sensor networks Sensors Fuzzy logic Base stations Cluster routing protocol energy-saving routing algorithm fuzzy inference system wireless sensor network

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An Efficient Class-Constrained DBSCAN Approach for Large-Scale Point Cloud clustering

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 7323-7332页

作者： Zhang, Hua Duan, Zhenwei Zheng, Nanshan Li, Yong Zeng, Yu Shi, Wenzhong China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China Sichuan Inst Coal Field Geol Engn Explorat & Desi Chengdu 610072 Peoples R China Sichuan Inst Coal Field Surveying & Mapping Engn Chengdu 610072 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China

To better interpret the scene and facilitate the subsequent processing of large-scale point cloud, clustering is often implemented in the preprocessing stage. However, when the original density-based spatial clustering of application with noise (DBSCAN) approach is used for point cloud clustering, it is easy to categorize closely spaced vegetation points and nonvegetation points into the same cluster by mistake. Aiming at the problem, this article presents an improved DBSCAN by embedding a strategy of class constraint, which is called CC-DBSCAN. Specially, based on the RGB and label information of each point in the training samples, by using the logistic regression model, the logistic regression color index (LRCI) is calculated for each point in the clustering samples. Then, points to be clustered are classified as vegetation points and nonvegetation points through the LRCI. Furtherly, the class information of point is introduced as a constraint for ensuring the core point and its directly density-reachable points belong to the same class, thus, solving the problem that confusion cluster of the adjacent vegetation points and nonvegetation points. We evaluate our approach on the benchmark SensatUrban dataset, where Cambridge_28 scene dataset is taken as the training set and Cambridge_18 scene dataset is as the dataset to be clustered. Experimental results show that our method achieved 97.20% purity of point cluster, which outperforms the other DBSCAN methods. At the same time, it takes only 24.25 s for clustering 2 million points, which indicates that CC-DBSCAN has high computational efficiency and good practicability.

关键词： Point cloud compression clustering algorithms Vegetation mapping Indexes Logistics Image color analysis Training Class constraint color index density-based spatial clustering of application with noise (DBSCAN) logical regression point cloud

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Secure and Evaluable clustering Based on a Multifunctional and Privacy-Preserving Outsourcing Computation Toolkit

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IEEE ACCESS 2022年 10卷 39407-39423页

作者： Li, Jialin Lu, Penghao Lin, Xuemin East China Normal Univ Sch Software Engn Shanghai 200062 Peoples R China

Although tremendous revolution has been made in the emerging cloud computing technologies over digital devices, privacy gradually becomes a big concern in outsourcing computation. Homomorphic encryption has been proposed to facilitate the preservation of data privacy while computational tasks being executed on ciphertext. However, many existing studies only support limited homomorphic calculation functions which barely satisfy complex computing tasks such as machine learning with massive computing resources and rich types of function. To address this problem, a novel multifunctional and privacy-preserving outsourcing computation toolkit is proposed in this paper, which supports several homomorphic computing protocols including division and power on ciphertext of integers and floating point numbers. Specifically, we first implement the homomorphic mutual conversion protocol between integer and floating point ciphertext to balance the efficiency and feasibility, considering the high-precision ciphertext operation on floating point numbers costs 100x computational overhead than that on integers. Second, we implement a homomorphic K-means algorithm based on our proposed toolkit for clustering and design the homomorphic silhouette coefficient as the evaluation index, thereby providing an informative cluster assessment for local users with limited resources. Then, we simulate the protocols of our proposed toolkit to explore the parameter sensitivity in terms of computational efficiency. Last, we report security analysis to prove the security of our toolkit without privacy leakage to unauthorized parties. Comprehensive experiments further demonstrate the efficiency and utility of our toolkit.

关键词： Outsourcing Task analysis Protocols Servers Privacy Cloud computing clustering algorithms Privacy-preserving secure outsourcing computation K-means silhouette coefficient

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