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Feature Discrimination of News Based on Canopy and KMGC-Search clustering

IEEE ACCESS 2022年 10卷 26307-26319页

作者： Shahroz, Mobeen Mushtaq, Muhammad Faheem Majeed, Rizwan Samad, Ali Mushtaq, Zaigham Akram, Urooj Islamia Univ Bahawalpur Dept Artificial Intelligence Bahawalpur 63100 Punjab Pakistan Islamia Univ Bahawalpur Directorate Informat Technol Bahawalpur 63100 Punjab Pakistan Islamia Univ Bahawalpur Dept Data Sci Bahawalpur 63100 Punjab Pakistan Islamia Univ Bahawalpur Dept Comp Sci Bahawalpur 63100 Punjab Pakistan

The internet provides a very vast amount of sources of news and the user has to search for desirable news by spending a lot of time because the user always prefers their related interest, desirable and informative news. The clustering of the news article having a great impact on the preferences of the user. The unsupervised learning techniques such that K-means clustering and Spectral clustering are proposed to categorize the news articles by extracting discriminant features that help the user to search and get informative news without wasting time. The BBC news articles dataset is used to perform experiments that consist of 2225 news articles. The TF-IDF feature extraction technique is used with K-means clustering and Spectral clustering to get the most similar clusters to categorize the news articles in respective domains. Those domains are sports, tech, entertainment, politics, and business. The clustering algorithms are evaluated using adjusted rand index, V-measure, homogeneity score, completeness score, and Fowlkes mallows score. The experimental results illustrated that K-means clustering performs better than spectral clustering using the TF-IDF feature extraction approach. But to improve the results the canopy centroid selection is used with the grid search optimization technique to optimize the results of the Kmeans and named its as a K-Means using Grid Search based on Canopy (KMGC-Search). The experimental results shows the proposed approach can be used as a viable method for the categorization of news articles.

关键词： Feature extraction clustering algorithms Machine learning Training Text mining Search engines Companies News categorization K-means clustering canopy machine learning TF-IDF grid search

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Efficient and Robust MultiView clustering With Anchor Graph Regularization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第9期32卷 6200-6213页

作者： Yang, Ben Zhang, Xuetao Lin, Zhiping Nie, Feiping Chen, Badong Wang, Fei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xi An Jiao Tong Univ Coll Artificial Intelligence Xian 710049 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Multi-view clustering has received widespread attention owing to its effectiveness by integrating multi-view data appropriately, but traditional algorithms have limited applicability to large-scale real-world data due to their high computational complexity and low robustness. Focusing on the aforementioned issues, we propose an efficient and robust multi-view clustering algorithm with anchor graph regularization (ERMC-AGR). In this work, a novel anchor graph regularization (ARG) is designed to improve the quality of the learned embedded anchor graph (EAG), and the obtained EAG is decomposed by nonnegative matrix factorization (NMF) under correntropy criterion to acquire clustering results directly. Different from the traditional graph regularization that needs to construct a large-scale Laplacian matrix pertaining to the all-sample graph, our lightweight AGR, constructed from the perspective of anchors, can reduce the computational complexity significantly while improving the EAG quality. Moreover, a factor matrix of NMF is constrained to be the cluster indicator matrix to omit additional k-means after optimization. Subsequently, correntropy is utilized to improve the effectiveness and robustness of ERMC-AGR owing to its promising performance to complex noises and outliers. Extensive experiments on real-world datasets and noisy datasets show that ERMC-ARG can improve the clustering efficiency and robustness while ensuring comparable or even better effectiveness.

关键词： Robustness clustering methods clustering algorithms Matrix decomposition Computational complexity Standards Optimization methods Multi-view clustering anchor graph regularization correntropy nonnegative matrix factorization

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Accurate Stock Price Forecasting Based on Deep Learning and Hierarchical Frequency Decomposition

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IEEE ACCESS 2024年 12卷 49878-49894页

作者： Li, Yi Chen, Lei Sun, Cuiping Liu, Guoxu Chen, Chunlei Zhang, Yonghui Weifang Univ Sch Econ & Management Weifang 261061 Peoples R China Weifang Univ Sci & Technol Sch Econ & Management Weifang 262700 Peoples R China Weifang Univ Sch Comp Engn Weifang 261061 Peoples R China

The stock market is playing an increasingly important role in the global economy. Accurate stock price forecasting not only aids the government in predicting economic trends but also helps investors anticipate higher expected returns. Nevertheless, hurdles such as nonlinearity, complexity and high volatility make it a daunting task to predict stock prices. To address this issue, this study proposes a new hybrid model, termed Hierarchical Decomposition-based Forecasting Model (HDFM), to decompose and forecast stock prices in a hierarchical fashion. The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for the initial decomposition of stock price time series. To enhance the predictive efficiency, sub-series with similar sample entropy from the decomposition were combined using the K-means clustering method. Through a thorough analysis, it was found that the first combined sub-series contained more high-frequency signals. Therefore, the first combined sub-series is subjected to a second decomposition with variational mode decomposition (VMD). Subsequently, the gated recurrent unit (GRU) model was used to individually predict each sub-series. The final results were obtained by merging the prediction outcomes. The proposed model was evaluated on three different stock markets. The experimental results show that the proposed model outperforms other forecasting methods across all stock indices. Moreover, ablation studies demonstrated the effectiveness of each component within the proposed model.

关键词： Predictive models Hidden Markov models Forecasting Autoregressive processes Deep learning Time series analysis Adaptation models Stock markets Hierarchical systems clustering algorithms Stock price prediction deep learning hierarchical decomposition clustering

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Automatic Generation of Multidimensional Labels of Educational Resources Based on Grey clustering 9th

Automatic Generation of Multidimensional Labels of Education...

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9th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2023

作者： Pan, Xiafu Shi, Yuanyuan Department of Public Safety Technology Hainan Vocational College of Political Science and Law Haikou571100 China Changchun University of Architecture and Civil Engineering Changchun130607 China

ISBN: (纸本)9783031514708

With the rapid development of Internet+education and the implementation of the "Three Connections and Two Platforms Project", online education resources are becoming more and more abundant, and society is increasingly dependent on online education resources. How to efficiently and reasonably classify resources and help users quickly access education resources is a research hotspot of online education. Therefore, an automatic generation method of multidimensional labels of educational resources based on gray clustering is proposed. The method first preprocesses educational resources, including word segmentation and de stop word processing. Feature words are extracted by combining TF-IDF and TextRank. Based on the extracted feature words, the improved KNN clustering algorithm CLKNN is used to cluster educational resources. The method of grey correlation degree is used to filter the text feature words that are clustered into the same type, and the selected feature words are used as their tag words to complete the automatic generation of multi-dimensional tags of educational resources. The results show that the accuracy P (Precision), recall rate R (Recall rate) and F-Measure of the automatic generation method of multi-dimensional labels of educational resources based on gray clustering are higher, which indicates that the research method can generate labels more comprehensively and accurately. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： clustering algorithms

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clustering Driven Iterated Hybrid Search for Vertex Bisection Minimization

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IEEE TRANSACTIONS ON COMPUTERS 2022年第10期71卷 2370-2380页

作者： Jin, Yan Xiong, Bowen He, Kun Hao, Jin-Kao Li, Chu-Min Fu, Zhang-Hua Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Univ Angers Dept Comp Sci LERIA F-49045 Angers France Inst Univ France F-75231 Paris France Univ Picardie Jules Verne MIS Fac Comp Sci F-80039 Amiens 01 France Chinese Univ Hong Kong Inst Robot & Intelligent Mfg Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China

The Vertex Bisection Minimization Problem (VBMP) is a relevant graph partitioning model with a variety of practical applications. This work introduces a clustering driven iterated hybrid search algorithm (CLUHS), which is the first approach that applies clustering to reinforce iterated local search for solving VBMP. The proposed CLUHS uses hierarchical clustering to build an initial solution, guide local search process and perform search diversification. Experimental studies on 137 benchmark instances show the high competitiveness of the proposed approach compared to the state-of-the-art methods. In particular, CLUHS finds new record-breaking solutions for 18 instances.

关键词： clustering algorithms Minimization Measurement Partitioning algorithms Search problems Perturbation methods Linear programming Vertex bisection graph partitioning clustering NP-hard local search

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A Two-Stage Strategy for UAV-Enabled Wireless Power Transfer in Unknown Environments

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第2期23卷 1785-1802页

作者： Shi, Junling Cong, Peiyu Zhao, Liang Wang, Xingwei Wan, Shaohua Guizani, Mohsen Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110136 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi U Arab Emirates

Due to the outstanding merits such as mobility, high maneuverability, and flexibility, Unmanned Aerial Vehicles (UAVs) are viable mobile power transmitters that can be rapidly deployed in geographically constrained regions. They are good candidates for supplying power to energy-limited Sensor Nodes (SNs) with Wireless Power Transfer (WPT) technology. In this paper, we investigate a UAV-enabled WPT system that transmits power to a set of SNs at unknown positions. A key challenge is how to efficiently gather the locations of SNs and design a power transfer scheme. We formulate a multi-objective optimization problem to jointly optimize these objectives: maximization of UAV's search efficiency, maximization of total harvested energy, minimization of UAV's flight energy consumption and maximization of UAV's energy utilization efficiency. To tackle these issues, we present a two-stage strategy that includes a UAV Motion Control (UMC) algorithm for obtaining the coordinates of SNs and a Dynamic Genetic clustering (DGC) algorithm for power transfer via grouping SNs into clusters. First, the UMC algorithm enables the UAV to autonomously control its own motion and conduct target search missions. The objective is to make the energy-restricted UAV find as many SNs as feasible without any priori known location information. Second, the DGC algorithm is used to optimize the energy consumption of the UAV by combining a genetic clustering algorithm with a dynamic clustering strategy to maximize the amount of energy harvested by SNs and the energy utilization efficiency of the UAV. Finally, experimental results show that our proposed algorithms outperform their counterparts.

关键词： Heuristic algorithms clustering algorithms Autonomous aerial vehicles Wireless sensor networks Mobile computing Genetics Energy consumption Energy consumption optimization sensor node (SN) target search two-stage strategy unmanned aerial vehicle (UAV) wireless power transfer (WPT)

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Construction and Evaluation of Information Granules: From the Perspective of clustering

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第3期52卷 2024-2037页

作者： Zhu, Xiubin Pedrycz, Witold Li, Zhiwu Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China

While granular computing has experienced rapid growth in the past decades and some milestones have been reached, a comprehensive study of the representation capabilities delivered by numeric prototypes and granular prototypes produced by different techniques still calls for comprehensive research and a comparative analysis. Well-constructed information granules are reflective of the nature of the numeric evidence and serve as backbones of granular classifiers and granular models. The objective of this study is to review a number of clustering paradigms aimed at the construction of information granules, discuss the development of granular prototypes, and conduct a comprehensive evaluation of quality of numeric prototypes and their corresponding augmentations coming in the form of granular prototypes. We have been witnessing many studies devoted to the construction of information granules, but a comparative analysis of the quality of information granules constructed on a basis of prototypes produced by different clustering algorithms is still lacking. In this regard, the review of the clustering algorithms supporting the formation of information granules and the comprehensive comparative study of their usefulness in classification and modeling tasks offered in this study make sense. This will promote the usage of information granules in various future works, especially classification problem and system modeling.

关键词： Prototypes clustering algorithms Systems modeling Prediction algorithms Numerical models Fuzzy sets Computational modeling clustering algorithm granular computing (GrC) information granule principle of justifiable granularity

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ESPP: Efficient Sector-Based Charging Scheduling and Path Planning for WRSNs With Hexagonal Topology

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第1期9卷 31-45页

作者： Naji, Abdulbary Hawbani, Ammar Wang, Xingfu Al-Gunid, Haithm M. Al-Dhabi, Yunes Al-Dubai, Ahmed Hussain, Amir Zhao, Liang Alsamhi, Saeed Hamood Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH11 4BN Scotland Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Univ Galway Insight Ctr Data Analyt Galway H91 TK33 Ireland IBB Univ Ibb 70270 Yemen

Wireless Power Transfer (WPT) is a promising technology that can potentially mitigate the energy provisioning problem for sensor networks. In order to efficiently replenish energy for these battery-powered devices, designing appropriate scheduling and charging path planning algorithms is essential and challenging. Whilst previous studies have tackled this challenge, the conjoint influences of network topology, charging path planning, and energy threshold distribution in Wireless Rechargeable Sensor Networks (WRSNs) are still in their infancy. We mitigate the aforementioned problem by proposing novel algorithmic solutions to efficient sector-based on-demand charging scheduling and path planning. Specifically, we first propose a hexagonal cluster-based deployment of nodes such that finding an NP-Complete Hamiltonian path is feasible. Second, each cluster is divided into multiple sectors and a charging path planning algorithm is implemented to yield a Hamiltonian path, aimed at improving the Mobile Charging Vehicle (MCV) efficiency and charging throughput. Third, we propose an efficient algorithm to calculate the importance of nodes to be used for charging duration decision-making and prioritization. Fourth, a non-preemptive dynamic priority scheduling algorithm is proposed for charging tasks' assignments and scheduling. Finally, extensive simulations have been conducted, revealing the significant advantages of our proposed algorithms in terms of energy efficiency, response time, dead nodes' density, and queuing processing.

关键词： Task analysis Path planning Network topology clustering algorithms Heuristic algorithms Topology Time factors Hexagonal-clustering path planning scheduling wireless power transfer wireless sensor networks wireless rechargeable sensor networks

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Hadoop Processing Methods for Large Scale Video Data

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IEEE ACCESS 2024年 12卷 176247-176258页

作者： Wu, Guangzhi Guangzhou Univ Party Propaganda Dept Guangzhou 510000 Peoples R China

As the continuous advancement of the We-media era, there are more and more short video users. However, there are still problems with low data processing efficiency and serious video damage in the current processing framework for large-scale video data. To address this issue, research is conducted to improve the artificial fish swarm algorithm using particle swarm optimization algorithm, and the improved algorithm is applied to the distributed system architecture Hadoop to enhance Hadoop's computation speed and accuracy of data. The study first conducts comparative experiments on the improved algorithm. The outcomes denoted that the accuracy of the improved algorithm was as high as 98.7%, while the error rate was only 0.9%. The recall and precision of video data were 92.1% and 96.5%, respectively, and the computational performance was significantly better than other comparative algorithms. The Hadoop data processing framework optimized after the improved algorithm was further tested, and the outcomes denoted that the response accuracy of the optimized data processing framework was as high as 98.6%, and the response time was only 0.32 seconds. From the above outcomes, the proposed improved algorithm can optimize the processing capability of the data processing framework for large-scale video data. Moreover, the optimized data processing framework can not only be used for large-scale video data processing, but also widely used in other fields to raise computational accuracy.

关键词： Fish Data processing Mathematical models Distributed databases Accuracy clustering algorithms Particle swarm optimization Tail Linear programming Flowcharts Video data particle swarm optimization algorithm artificial fish swarm algorithm Hadoop framework data processing

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Partially-Supervised Metric Learning via Dimensionality Reduction of Text Embeddings Using Transformer Encoders and Attention Mechanisms

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IEEE ACCESS 2024年 12卷 77536-77554页

作者： Hodgson, Ryan Wang, Jingyun Cristea, Alexandra I. Graham, John Univ Durham Dept Comp Sci Durham DH1 3LE England Reveela Technol Newcastle Upon Tyne NE1 6UF Northumberland England

Real-world applications of word embeddings to downstream clustering tasks may experience limitations to performance, due to the high degree of dimensionality of the embeddings. In particular, clustering algorithms do not scale well when applied to highly dimensional data. One method to address this is through the use of dimensionality reduction algorithms (DRA). Current state of the art algorithms for dimensionality reduction (DR) have been demonstrated to contribute to improvements in clustering accuracy and performance. However, the impact that a neural network architecture can have on the current state of the art Parametric Uniform Manifold Approximation and Projection (UMAP) algorithm is yet unexplored. This work investigates, for the first time, the effects of using attention mechanisms in neural networks for Parametric UMAP, through the application of network architectures that have had considerable effect upon the wider machine learning and natural language processing (NLP) fields - namely, the transformer-encoder, and the bidirectional recurrent neural network. We implement these architectures within a semi-supervised metric learning pipeline, with results demonstrating an improvement in the clustering accuracy, compared to conventional DRA techniques, on three out of four datasets, and comparable SoA accuracy on the fourth. To further support our analysis, we also investigate the effects of the transformer-encoder metric-learning pipeline upon the individual class accuracy of downstream clustering, for highly imbalanced datasets. Our analyses indicate that the proposed pipeline with transformer-encoder for parametric UMAP confers a significantly measurable benefit to the accuracy of underrepresented classes.

关键词： Dimensionality reduction Task analysis clustering algorithms Neural networks Transformers Measurement Supervised learning attention mechanisms clustering transformer networks metric learning

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