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A New Density Peak clustering Algorithm Based on Cluster Fusion Strategy

IEEE ACCESS 2022年 10卷 98034-98047页

作者： Li, Fuxiang Zhou, Ming Li, Shu Yang, Tianhao Harbin Univ Sci & Technol Sch Sci Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Elect & Elect Engn Minist Educ Key Lab Engn Dielect & Applicat Harbin 150080 Peoples R China

When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and inter-cluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.

关键词： clustering algorithms Resource management Partitioning algorithms Classification algorithms Solid modeling Image segmentation Complexity theory Density measurement clustering density peaks candidate cluster center cluster fusion strategy

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An Ensemble Learning Algorithm Based on Density Peaks clustering and Fitness for Imbalanced Data

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IEEE ACCESS 2022年 10卷 116120-116128页

作者： Xu, Hui Liu, Qicheng Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

In view of the low classification accuracy of the minority class in imbalanced data, an algorithm called DPF-EL (density peaks and fitness combined with ensemble learning) based on density peaks clustering and fitness is proposed. Firstly, this method uses the density peaks clustering algorithm to divide the majority class into different sub-clusters, the local density calculated in the clustering process is used to assign weights to each sub-cluster, and the number of under-sampling is determined by the weights. Secondly, the concept of fitness is introduced into the sub-clusters, the selection probability of the samples is calculated according to the size of their fitness, and the majority class is under-sampled based on the selection probability. Finally, combined with boosting algorithm, iterative training is performed on the balanced data set. Experimental tests were conducted with KEEL imbalanced data sets, and the experimental results show that the performance of DPF-EL algorithm is better than other algorithms, which indicates the feasibility of the proposed algorithm.

关键词： Classification algorithms clustering algorithms Probability Ensemble learning Partitioning algorithms Machine learning algorithms Decision trees Imbalanced data density peaks clustering fitness under-sampling classification

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MMCo-Clus - An Evolutionary Co-clustering Algorithm for Gene Selection

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第9期34卷 4371-4384页

作者： Cui, Laizhong Acharya, Sudipta Mishra, Sumit Pan, Yi Huang, Joshua Zhexue Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China IIIT Guwahati Dept Comp Sci & Engn Gauhati 781039 Assam India Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

In the era of Big Data, cluster analysis of high-dimensional data sets often suffers from the Curse of dimensionality. To overcome this problem, the dimensionality reduction through feature selection becomes inevitable. Co-clustering or two-way clustering is considered to be a more sophisticated tool than conventional one-way clustering. Moreover, the advent of multi-view learning shows that the subjects of a data set can be interpreted in many ways. Interestingly, a minimal number of existing feature selection algorithms take advantage of the co-clustering method and are designed to consider multi-view data. Motivated by this, in the current article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a multi-objective optimization based multi-view Co-clustering algorithm (named MMCo-Clus). A popular evolutionary technique - Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been utilized as the proposed method's underlying optimization strategy. First, we construct two views of a chosen data set, utilizing knowledge from two different biological data sources. Next, we develop the MMCo-Clus algorithm considering the constructed views to identify a set of "good" co-clustering solutions. Finally, based on a concept of consensus operation on the co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data sets. Our feature selection method's effectiveness is evaluated through sample-classification accuracy, accompanied by the cluster profile plot/Eisen plot/t-SNE plot, and biological/statistical significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports our proposed me

关键词： Feature extraction Germanium clustering algorithms Biology Optimization Data analysis Genetic algorithms Feature selection co-clustering multi-view learning multi-objective optimization sample classification

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A Data-Driven Three-Stage Adaptive Pattern Mining Approach for Multi-Energy Loads

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 7455-7467页

作者： Guo, Yixiu Li, Yong Zhou, Sisi Zhang, Zhenyu Li, Zuyi Shahidehpour, Mohammad Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China IIT Galvin Ctr Elect Innovat Chicago IL 60616 USA

In-depth understanding of the multi-energy consumption behavior pattern is the essential to improve the management of multi-energy system (MES). This paper proposes a data-driven three-stage adaptive pattern mining approach for multi-energy loads, which addresses the issues of complex multi-dimensional time-series mining, uncommon daily loads discovery, typical load classification and parameter setting requiring user intervention. In the first stage, the relative state changes over time between different energy loads are excavated based on Autoplait, which realizes time pattern discovery, segmentation and match for multi-dimensional loads. In the second stage, adaptive affinity propagation (AAP) considering trend similarity distance (TSD) is proposed to classify loads into common and uncommon clusters, where uncommon loads are eliminated and daily pattern is obtained by taking average of common loads. In the third stage, AAP with windows dynamic time warping (WDTW) identifies various profiles to obtain typical pattern of daily loads. Specifically, pattern mining provides the key information of multi-energy loads, which is significant to the applications for the demand side, such as load scene compression, load forecasting and demand response analysis. A case study uses MES data from Arizona State University to verify the effectiveness and practicality of the proposed approach.

关键词： Market research Data mining Time series analysis Hidden Markov models clustering algorithms Pattern matching Excavation Multi-energy loads multi-dimensional time series pattern mining demand response analysis affinity propagation

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High-Precision Underwater Perception and Path Planning of AUVs Based on Quantum-Enhanced

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第3期70卷 5607-5617页

作者： Xi, Meng Wang, Zhijing He, Jingyi Wang, Yibo Wen, Jiabao Xiao, Shuai Yang, Jiachen Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

With the rapid development of society, a wide variety of consumer applications are increasingly emerging. At the same time, the involvement of intelligent technologies such as deep learning, reinforcement learning, and quantum computing is empowering consumer applications by driving them to be smarter, more secure, and digitized. Among them, the underwater field is an important application direction, such as equipment overhaul, scientific research, resource exploration, and so on. This paper targets the detection, optimization, and inference tasks in underwater applications, aiming to design efficient and safe solution algorithms for them using new techniques. First, we establish an underwater mission scenario, using time-varying current data to create a 3D ocean environment model, which can satisfy the requirements of different underwater applications. Second, a safe and efficient underwater object detection algorithm is designed, which constructs a deep neural network to extract valid information from redundant environments. Finally, a path planning algorithm for underwater unmanned equipment clusters is developed to solve the optimization decision problem through deep reasoning computation. We carry out a series of comparative experiments, which adequately prove that the algorithm proposed in this paper has good superiority, can cope with the interference of different intensities of ocean currents, and ensures the operational effect of the cluster formation.

关键词： Reinforcement learning Heuristic algorithms Path planning clustering algorithms Task analysis Transformers Robots Consumer applications underwater operation reinforcement learning quantum-enhanced

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Resource Allocation for Joint Communication and Positioning in mmWave Ad Hoc Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第2期73卷 2187-2201页

作者： Luo, Xueni Lu, Xiaofeng Yin, Benquan Yang, Kun Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

Joint communication and positioning will be a critical driver in future wireless networks for emerging application areas. Supporting mobility, Ad-hoc networks can freely and dynamically self-organize an arbitrary and temporary network topology without any pre-existing infrastructure. Combined with millimeter-wave (mmWave), Ad-hoc networks can construct communication links with less time and higher directivity due to directional antennas and building blockage. The wide spectrum of mmWave could provide a high-oriented channel for positioning, which is significant for multi-user conditions. In this article, we concentrate on high-efficiency algorithms to allocate spectrum and power to different services and achieve a performance tradeoff between the communication and positioning process. Besides, the severe interference between users would degrade the actual system performance. To address these challenges, this article proposes an optimal clustering algorithm based on the mmWave Line of Sight (LoS) probability to form two different sub-nets for communication and positioning services, respectively. Then, the available spectrum resources are divided into two parts for the above sub-nets under the Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM) technique, which could design sub-bands independently. Finally, we proposed an optimal algorithm to allocate sub-bands and power to improve the performance of the communication sub-net while guaranteeing the positioning performance in the corresponding sub-net. Numeric simulation results demonstrate that the proposed resource allocation algorithm could achieve better performance both in the communication and position process.

关键词： Millimeter wave communication Ad hoc networks Resource management OFDM Interference clustering algorithms Linear antenna arrays Joint communication and positioning millimeter-wave clustering algorithm resource allocation Ad hoc network

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A Fast Hybrid Feature Selection Based on Correlation-Guided clustering and Particle Swarm Optimization for High-Dimensional Data

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9573-9586页

作者： Song, Xian-Fang Zhang, Yong Gong, Dun-Wei Gao, Xiao-Zhi China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Univ Eastern Finland Sch Comp Kuopio 70210 Finland

The ``curse of dimensionality'' and the high computational cost have still limited the application of the evolutionary algorithm in high-dimensional feature selection (FS) problems. This article proposes a new three-phase hybrid FS algorithm based on correlation-guided clustering and particle swarm optimization (PSO) (HFS-C-P) to tackle the above two problems at the same time. To this end, three kinds of FS methods are effectively integrated into the proposed algorithm based on their respective advantages. In the first and second phases, a filter FS method and a feature clustering-based method with low computational cost are designed to reduce the search space used by the third phase. After that, the third phase applies oneself to finding an optimal feature subset by using an evolutionary algorithm with the global searchability. Moreover, a symmetric uncertainty-based feature deletion method, a fast correlation-guided feature clustering strategy, and an improved integer PSO are developed to improve the performance of the three phases, respectively. Finally, the proposed algorithm is validated on 18 publicly available real-world datasets in comparison with nine FS algorithms. Experimental results show that the proposed algorithm can obtain a good feature subset with the lowest computational cost.

关键词： clustering algorithms Computational efficiency Particle swarm optimization Feature extraction Search problems Convergence Mutual information clustering feature selection (FS) hybrid search particle swarm optimization (PSO)

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Fast Multiview clustering With Spectral Embedding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 3884-3895页

作者： Yang, Ben Zhang, Xuetao Nie, Feiping Wang, Fei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xi An Jiao Tong Univ Natl Engn Lab Visual Informat Proc & Applicat Xian 710049 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

Spectral clustering has been a hot topic in unsupervised learning owing to its remarkable clustering effectiveness and well-defined framework. Despite this, due to its high computation complexity, it is unable of handling large-scale or high-dimensional data, particularly multi-view large-scale data. To address this issue, in this paper, we propose a fast multi-view clustering algorithm with spectral embedding (FMCSE), which speeds up both the spectral embedding and spectral analysis stages of multi-view spectral clustering. Furthermore, unlike conventional spectral clustering, FMCSE can acquire all sample categories directly after optimization without extra k-means, which can significantly enhance efficiency. Moreover, we also provide a fast optimization strategy for solving the FMCSE model, which divides the optimization problem into three decoupled small-scale sub-problems that can be solved in a few iteration steps. Finally, extensive experiments on a variety of real-world datasets (including large-scale and high-dimensional datasets) show that, when compared to other state-of-the-art fast multi-view clustering baselines, FMCSE can maintain comparable or even better clustering effectiveness while significantly improving clustering efficiency.

关键词： Spectral analysis Optimization Complexity theory clustering methods clustering algorithms Linear programming Task analysis Multi-view clustering spectral embedding anchor graph orthogonality

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GenSumm: A Joint Framework for Multi-Task Tweet Classification and Summarization Using Sentiment Analysis and Generative Modelling

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第4期15卷 1838-1855页

作者： Bansal, Diksha Grover, Rahul Saini, Naveen Saha, Sriparna Indian Inst Technol Dept Comp Sci & Engn Patna 801106 Bihar India Indian Inst Informat Technol Dept Informat Technol Allahabad 211015 Uttar Pradesh India

Social media platforms like Twitter act as the medium for communication among people, government agencies, NGOs, and other relief providing agencies in widespread humanitarian havoc during a disaster outbreak when other communication means might not be available. Various agencies leverage Twitter's open and public features to get timely and reliable updates, thus support agencies in communicating with the people on rescue and provide immediate relief. As situational updates are mixed in millions of other tweets, an efficient system is required to extract and summarize these tweets. We have developed a novel framework that uses a deep learning-based classification model to separate the informational tweets from others and summarizes them in the current paper. Non-situational tweets mostly comprise sentiments like grief, anger, sorrow, etc. Motivated by this observation, we have solved sentiment classification and informative tweet selection tasks simultaneously using a multi-task learning (MTL) in a deep-learning framework. Our summarization approach generates clustering solutions using various existing approaches and then ensembles cluster solutions using generative modelling. A summary is formulated by extracting tweets from different clusters. The proposed approach's superior performance on four disaster-related events indicates the developed framework's efficiency over the state-of-the-art techniques.

关键词： Task analysis Feature extraction Social networking (online) Sentiment analysis Blogs clustering algorithms Self-organizing feature maps Summarization classification convolution neural network clustering generative modelling

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Dynamic Sparse Subspace clustering for Evolving High-Dimensional Data Streams

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 4173-4186页

作者： Sui, Jinping Liu, Zhen Liu, Li Jung, Alexander Li, Xiang Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Aalto Univ Dept Comp Sci Espoo 02150 Finland Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu 02150 Finland Natl Univ Def Technol Dept Elect Sci Changsha 410073 Peoples R China

In an era of ubiquitous large-scale evolving data streams, data stream clustering (DSC) has received lots of attention because the scale of the data streams far exceeds the ability of expert human analysts. It has been observed that high-dimensional data are usually distributed in a union of low-dimensional subspaces. In this article, we propose a novel sparse representation-based DSC algorithm, called evolutionary dynamic sparse subspace clustering (EDSSC). It can cope with the time-varying nature of subspaces underlying the evolving data streams, such as subspace emergence, disappearance, and recurrence. The proposed EDSSC consists of two phases: 1) static learning and 2) online clustering. During the first phase, a data structure for storing the statistic summary of data streams, called EDSSC summary, is proposed which can better address the dilemma between the two conflicting goals: 1) saving more points for accuracy of subspace clustering (SC) and 2) discarding more points for the efficiency of DSC. By further proposing an algorithm to estimate the subspace number, the proposed EDSSC does not need to know the number of subspaces. In the second phase, a more suitable index, called the average sparsity concentration index (ASCI), is proposed, which dramatically promotes the clustering accuracy compared to the conventionally utilized SCI index. In addition, the subspace evolution detection model based on the Page-Hinkley test is proposed where the appearing, disappearing, and recurring subspaces can be detected and adapted. Extinct experiments on real-world data streams show that the EDSSC outperforms the state-of-the-art online SC approaches.

关键词： clustering algorithms Indexes Heuristic algorithms Data models Adaptation models Task analysis Data structures Data stream clustering (DSC) high-dimensional data stream sparse representation subspace clustering (SC)

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