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Multiview Subspace clustering Based on Adaptive Global Affinity Graph Learning

JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL 2022年第1期61卷 24-37页

作者： Chen, X. Zhu, D. Wang, L. Zhu, Y. Matveev, I. A. Nanjing Univ Aeronaut & Astronaut Sci Coll Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Lab Math Modeling & High Performance Comp Aircraf Nanjing Peoples R China Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia Nanjing Univ Aeronaut & Astronaut Fac Expt Teaching Nanjing Peoples R China

Multiview subspace clustering is a hot topic in machine learning. Most existing methods perform clustering based on a predefined affinity graph constructed from the neighbor information of the data., which affects the performance of clustering greatly. A method is proposed to solve this problem by constructing adjacency graphs in each of the feature spaces and a common object affinity graph. A sequence of iterations is performed, at each of which the adjacency graphs and the affinity graph are refined. A restriction is also imposed on the rank of the Laplace matrix of the affinity graph, which, according to a well-known theorem, ensures the partition of the graph into several connected components, which, after the completion of the iterations, are considered the required clusters. In the numerical experiments, several test bases from open sources are used. The results are compared with the known methods, and some advantage of the proposed approach is obtained.

关键词： clustering algorithms

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Machine learning based tomographic image reconstruction technique to detect hollows in wood

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WOOD SCIENCE AND TECHNOLOGY 2024年第4期58卷 1491-1516页

作者： Yildizcan, Ecem Nur Ari, Mehmet Erdi Tunga, Burcu Gelir, Ali Kurul, Fatih As, Nusret Dundar, Turker Istanbul Tech Univ Fac Arts & Sci Math Engn Istanbul Turkiye Istanbul Univ Fac Arts & Sci Comp Sci Istanbul Turkiye Istanbul Tech Univ Fac Arts & Sci Math Engn Istanbul Turkiye Istanbul Univ Cerrahpasa Fac Forestry Istanbul Turkiye

A new technique based on machine learning algorithms was introduced to detect internal wood defects. This technique relies on analyzing segmented propagation rays of stress waves and successfully generates the tomographic images of the defects by using the stress wave velocity. Utilizing a dual-stage methodology, the initial phase involves ray segmentation for the precise delineation of stress wave propagation, while the subsequent stage integrates advanced classification and clustering algorithms to facilitate the generation of tomographic images. This approach effectively tackles the inherent challenges associated with accurate segmentation and classification of stress wave velocity rays. The effectiveness of the proposed method was evaluated using both synthetic and experimental data. The results showed that the proposed method, when compared with some state-of-the-art methods, has a superior ability to accurately detect defective regions in the wood. The success of the proposed method is evaluated with four different evaluation metrics. It determined that over 90% success is achieved for all metrics. In comparison with related studies, it determined that the results are improved by 7-22% compared to the literature.

关键词： clustering algorithms

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Depth-Based Condition Monitoring and Contributing Factor Analysis for Anomalies in Combined Cycle Power Plant

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IEEE ACCESS 2024年 12卷 73400-73412页

作者： Lim, Munwon Kim, Yunhee Jin, Shengxin Ha, Sangwoo Yong Chang, Sung Su Kang, Hae Sang Park, Gyu Lee Joo, Mi Jung, Chang-Sik Cho, Youngjin Bae, Suk Joo Hanyang Univ Dept Ind Engn Seoul 04763 South Korea Korea Elect Power Res Inst Daejeon 34056 South Korea Rozetatech Seongnam 13440 South Korea

Unexpected fault or failure in the power plant have caused high maintenance costs, the loss of energy production, and even safety issues. Developments in sensor technologies and data analytics have aided proper preventive maintenance actions for the system to improve asset availability and reduce repair costs. Nevertheless, effective condition monitoring of a power plant experiences a considerable nuisance from challenging issues such as inherent data characteristics such as high correlations between process variables, irrelevant information from environmental noises, and system complexity. To resolve these problems, this paper proposes an integrated monitoring scheme for performing efficient corrective actions by identifying the variables related to anomalies in combined cycle power plants. The scheme includes a clustering-based linear discriminant analysis to extract key variables for reducing dimensionality to efficiently handle the data, followed by employing the Mahalanobis depth statistics for anomaly detection and causal analysis via contribution scores. The proposed monitoring scheme is applied to condition monitoring data of a combined cycle power plant in South Korea, which include two types of anomalous operations. The reliability and robustness of the proposed condition monitoring scheme are validated by comparing other state-of-the-art methods. The proposed method shows a potential in efficiently detecting anomalies during operation and even early detecting the precursors of anomalies. It is expected to prevent imminent faults or failures by taking proper actions to relevant key process parameters of combined cycle power plant in advance.

关键词： Power generation Condition monitoring clustering algorithms Monitoring Maintenance Analytical models Turbines Anomaly detection Medical services Biomedical monitoring causal inference condition-based maintenance contribution scores health monitoring

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Learnable Weighting of Intra-Attribute Distances for Categorical Data clustering with Nominal and Ordinal Attributes

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第7期44卷 3560-3576页

作者： Zhang, Yiqun Cheung, Yiu-ming Guangdong Univ Technol Sch Comp Guangzhou 510080 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing clustering methods treat the two categorical subtypes, i.e., nominal and ordinal attributes, in the same way when calculating the dissimilarity without considering the relative order information of the ordinal values. Moreover, there would exist interdependence among the nominal and ordinal attributes, which is worth exploring for indicating the dissimilarity. This paper will therefore study the intrinsic difference and connection of nominal and ordinal attribute values from a perspective akin to the graph. Accordingly, we propose a novel distance metric to measure the intra-attribute distances of nominal and ordinal attributes in a unified way, meanwhile preserving the order relationship among ordinal values. Subsequently, we propose a new clustering algorithm to make the learning of intra-attribute distance weights and partitions of data objects into a single learning paradigm rather than two separate steps, whereby circumventing a suboptimal solution. Experiments show the efficacy of the proposed algorithm in comparison with the existing counterparts.

关键词： clustering algorithms Weight measurement Measurement Loss measurement Encoding Task analysis Partitioning algorithms Categorical data clustering nominal-and-ordinal attribute intra-attribute distance learnable weighting

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Research on Intelligent clustering Scoring of English Text Based on XGBOOST Algorithm 14th

Research on Intelligent Clustering Scoring of English Text B...

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Zeng, Zhaolian Yao, Wanyi Zeng, Jia Lei, Jiawei Chen, Feiyun Wen, Peihua School of Foreign Studies South China Agricultural University Guangzhou510642 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

ISBN: (纸本)9789819743957

In today's explosive growth of online text data, how to feature mine text data from a wide range of sources, cluster text data with similar features, and classify them according to their features has become a hot issue. In this paper, according to the characteristics of English text data, considering the existence of a large number of redundant features in English text, the existence of features with similar meanings in English text and other problems, the following method is proposed: in the first step, the English text is segmented, data preprocessing is carried out, and then the text data is processed by using the TF-IDF algorithm, to get the roughly selected subset of features;in the second step, the improved Binary Particle Swarm Optimization algorithm is used to In the second step, the improved binary particle swarm optimization algorithm GSBPSO (Global-to-local Searching-based BinaryParticle Swarm Optimization) is used to re-select the features of the roughly selected feature subset obtained in the first step to obtain the optimal feature subset;in the third step, the binary SKM clustering algorithm is used to cluster English text according to the optimal feature subset obtained in the second step. English text is clustered according to the optimal feature subset obtained in the second step, and the classification criteria are formulated for the existing text corpus to categorize the text data. Finally, the XGBOOST algorithm is applied to evaluate the prediction and establish the intelligent clustering scoring model of English text based on XGBOOST. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： clustering algorithms

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Joint NOMA clustering and Power Allocation in IoRT-Oriented Satellite Terrestrial Relay Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第10期71卷 11078-11088页

作者： Zhao, Bo Ren, Guangliang Dong, Xiaodai Northwestern Ploytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada

In this paper, a joint non-orthogonal multiple access (NOMA) clustering and power allocation problem is studied to maximize the uplink total sum rate in Internet of Remote Things (IoRT)-oriented satellite terrestrial relay networks (STRNs). The joint optimization problem is a mixed-integer programming (MIP) problem, which is non-convex and NP-hard. To solve this problem, we decompose it into two subproblems and propose staged algorithms to solve them. The first subproblem is an optimal NOMA clustering problem, which is still non-convex and NP-hard. To solve the subproblem efficiently, a reinforcement learning-based dynamic clustering algorithm (RL-DCA) is proposed. Using the RL-DCA, the users are able to gradually learn the optimal NOMA clustering policy in a distributed fashion. The RL-DCA has fast convergence and its computational complexity at each user remains fixed for any network size. The second subproblem is an intra-cluster optimal NOMA power allocation problem, which is still non-convex. To solve it, we firstly convert it into a convex problem by constrait approximation, then use the Karush-Kuhn-Tucker (KKT) conditions based algorithm to find the optimal power allocation policy. The second subproblem is solved in an offline fashion, which enables low computational complexity in transmission. Simulation results show that: 1) The RL-DCA has fast convergence and enables good clustering performance;2) The joint NOMA clustering and power allocation scheme greatly outperforms the orthogonal multiple access (OMA) scheme and other NOMA schemes in terms of total sum rate.

关键词： NOMA Relays Uplink Resource management Satellites System performance clustering algorithms Satellite terrestrial relay networks Internet of Remote Things NOMA clustering power allocation joint optimization

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Joint Representation Learning and clustering: A Framework for Grouping Partial Multiview Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3826-3840页

作者： Zhuge, Wenzhang Tao, Hong Luo, Tingjin Zeng, Ling-Li Hou, Chenping Yi, Dongyun Natl Univ Def Technol Coll Liberal Arts & Sci Changsha 410073 Peoples R China Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Peoples R China Hunan First Normal Univ Sch Math & Comp Sci Changsha 410205 Peoples R China

Partial multi-view clustering has attracted various attentions from diverse fields. Most existing methods adopt separate steps to obtain unified representations and extract clustering indicators. This separate manner prevents two learning processes to negotiate to achieve optimal performance. In this paper, we propose the Joint Representation Learning and clustering (JRLC) framework to address this issue. The JRLC framework employs representation matrices to extract view-specific clustering information directly from the presence of partial similarity matrices, and rotates them to learn a common probability label matrix simultaneously, which connects representation learning and clustering seamlessly to achieve better clustering performance. Under the guidance of JRLC framework, several new incomplete multi-view clustering methods can be developed by extending existing single-view graph-based representation learning methods. For illustration, within the framework, we propose two specific methods, JRLC with spectral embedding (JRLC-SE) and JRLC via integrating nonnegative embedding and spectral embedding (JRLC-NS). Two iterative algorithms with guaranteed convergence are designed to solve the resultant optimization problems of JRLC-SE and JRLC-NS. Experimental results on various datasets and news topic clustering application demonstrate the effectiveness of the proposed algorithms.

关键词： Kernel clustering algorithms clustering methods Optimization Iterative methods Convergence Task analysis Representation learning clustering partial multi-view data graph

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Multiview Subspace Dual clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7425-7437页

作者： Luo, Shirui Cao, Xiaochun Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Cyberspace Secur Res Ctr Peng Cheng Lab Shenzhen 518055 Peoples R China

A single clustering refers to the partitioning of data such that the similar data are assigned into the same group, whereas the dissimilar data are separated into different groups. Recently, multiview clustering has received significant attention in recent years. However, most existing works tackle the single-clustering scenario, which only use single clustering to partition the data. In practice, nevertheless, the real-world data are complex and can be clustered in multiple ways depending on different interpretations of the data. Unlike these methods, in this article, we apply dual clustering to multiview subspace clustering. We propose a multiview dual-clustering method to simultaneously explore consensus representation and dual-clustering structure in a unified framework. First, multiview features are integrated into a latent embedding representation through a multiview learning process. Second, the dual-clustering segmentation is incorporated into the subspace clustering framework. Finally, the learned dual representations are assigned to the corresponding clusterings. The proposed approach is efficiently solved using an alternating optimization scheme. Extensive experiments demonstrate the superiority of our method on real-world multiview dual- and single-clustering datasets.

关键词： Optimization clustering algorithms Laplace equations Faces Sparse matrices Learning systems Image segmentation Dual-clustering learning multiview learning subspace clustering

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Fast Self-Supervised clustering With Anchor Graph

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4199-4212页

作者： Wang, Jingyu Ma, Zhenyu Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Astronaut Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the real world, unsupervised learning has been regarded as a speedy and powerful strategy on clustering tasks. However, clustering directly from primal data sets leads to high computational cost, which limits its application on large-scale and high-dimensional problems. Recently, anchor-based theories are proposed to partly mitigate this problem and field naturally sparse affinity matrix, while it is still a challenge to get excellent performance along with high efficiency. To dispose of this issue, we first presented a fast semisupervised framework (FSSF) combined with a balanced K-means-based hierarchical K-means (BKHK) method and the bipartite graph theory. Thereafter, we proposed a fast self-supervised clustering method involved in this crucial semisupervised framework, in which all labels are inferred from a constructed bipartite graph with exactly k connected components. The proposed method remarkably accelerates the general semisupervised learning through the anchor and consists of four significant parts: 1) obtaining the anchor set as interim through BKHK algorithm;2) constructing the bipartite graph;3) solving the self-supervised problem to construct a typical probability model with FSSF;and 4) selecting the most representative points regarding anchors from BKHK as an interim and conducting label propagation. The experimental results on toy examples and benchmark data sets have demonstrated that the proposed method outperforms other approaches.

关键词： Sparse matrices Bipartite graph clustering algorithms Task analysis Unsupervised learning Data models Supervised learning Bipartite graph label propagation self-supervised learning semisupervised framework special selection

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Double-Layer K-Means plus plus clustering Method for Evaluation of Dispatchable Potential of Massive Regional 5G Base Stations

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IEEE ACCESS 2022年 10卷 82870-82882页

作者： Zhong, Lijun Zhu, Mengting Chen, Shuiyao Shi, Minda Zhang, Xianglong Chen, Ding State Grid Zhejiang Elect Power Co Jiaxing Power Supply Co Jiaxing 314000 Peoples R China Zhejiang Univ Coll Elect & Engn Hangzhou 310027 Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou 310000 Peoples R China State Grid Econ & Technol Res Inst Co Ltd Beijing 102209 Peoples R China

5G base stations (BSs), which are the essential parts of the 5G network, are important user-side flexible resources in demand response (DR) for electric power system. However, a 5G BS has little and difference dispatchable potential, how to make massive 5G BSs participate in DR conveniently is an urgent problem to be solved. clustering is an effective solution. Aiming at the special requirements of big data analysis and dispatching difficulties brought by the massive and ultra-dense distribution of 5G BSs, this paper proposes a double-layer K-means++ clustering method for regional 5G BSs considering the main characteristics of 5G BSs. The proposed clustering method considers both the geographical location and power consumption characteristics of the regional 5G BSs to partition the 5G BSs into appropriate number of clusters. Besides, the comprehensive charging and discharging dispatchable potential evaluation indices are established based on the clustering results to evaluate the dispatchable potential of each cluster. The effectiveness of the proposed method and the dispatchable potential evaluation indices are verified through a case of 5000 5G BSs in Jiaxing, China. The simulation findings reveal that the performance of the proposed clustering method is better than classical clustering algorithm and the dispatchable potential of the 5G BSs clusters can be accurately analyzed by the proposed comprehensive indices, which lay the foundation for friendly and fine interaction between 5G BSs and the power grid.

关键词： 5G mobile communication Power demand Batteries clustering methods Base stations clustering algorithms Indexes 5G base station K-means plus plus clustering dispatchable potential evaluation big data analysis demand response

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