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  • Open Access

    ARTICLE

    Relative-Density-Viewpoint-Based Weighted Kernel Fuzzy Clustering

    Yuhan Xia1, Xu Li1, Ye Liu1, Wenbo Zhou2, Yiming Tang1,3,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 625-651, 2025, DOI:10.32604/cmc.2025.065358 - 09 June 2025

    Abstract Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology. The combination of domain knowledge and fuzzy clustering algorithms has some problems, such as initialization sensitivity and information granule weight optimization. Therefore, we propose a weighted kernel fuzzy clustering algorithm based on a relative density view (RDVWKFC). Compared with the traditional density-based methods, RDVWKFC can capture the intrinsic structure of the data more accurately, thus improving the initial quality of the clustering. By introducing a Relative Density based Knowledge Extraction Method (RDKM) and adaptive weight optimization mechanism, we effectively solve the… More >

  • Open Access

    ARTICLE

    Short-Term Electricity Load Forecasting Based on T-CFSFDP Clustering and Stacking-BiGRU-CBAM

    Mingliang Deng1, Zhao Zhang1,*, Hongyan Zhou2, Xuebo Chen2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1189-1202, 2025, DOI:10.32604/cmc.2025.064509 - 09 June 2025

    Abstract To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes… More >

  • Open Access

    ARTICLE

    Multi-View Picture Fuzzy Clustering: A Novel Method for Partitioning Multi-View Relational Data

    Pham Huy Thong1, Hoang Thi Canh2,3,*, Luong Thi Hong Lan4, Nguyen Tuan Huy4, Nguyen Long Giang1,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5461-5485, 2025, DOI:10.32604/cmc.2025.065127 - 19 May 2025

    Abstract Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex, high-dimensional data that single-view methods cannot capture. Traditional fuzzy clustering techniques, such as Fuzzy C-Means (FCM), face significant challenges in handling uncertainty and the dependencies between different views. To overcome these limitations, we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data, aiming to enhance clustering accuracy and robustness, termed Multi-view Picture Fuzzy Clustering (MPFC). In particular, the picture fuzzy set theory extends the capability to… More >

  • Open Access

    ARTICLE

    LP-CRI: Label Propagation Immune Generation Algorithm Based on Clustering and Rebound Mechanism

    Hao Huang1, Kongyu Yang2,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5373-5391, 2025, DOI:10.32604/cmc.2025.063311 - 19 May 2025

    Abstract Many existing immune detection algorithms rely on a large volume of labeled self-training samples, which are often difficult to obtain in practical scenarios, thus limiting the training of detection models. Furthermore, noise inherent in the samples can substantially degrade the detection accuracy of these algorithms. To overcome these challenges, we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation (LP-CRI). The dataset is randomly partitioned into multiple subsets, each of which undergoes clustering followed by label propagation and evaluation. The rebound mechanism assesses the model’s performance after propagation and More >

  • Open Access

    ARTICLE

    Adaptive Multi-Objective Energy Management Strategy Considering the Differentiated Demands of Distribution Networks with a High Proportion of New-Generation Sources and Loads

    Huang Tan1, Haibo Yu1, Tianyang Chen1, Hanjun Deng2, Yetong Hu3,*

    Energy Engineering, Vol.122, No.5, pp. 1949-1973, 2025, DOI:10.32604/ee.2025.062574 - 25 April 2025

    Abstract With the increasing integration of emerging source-load types such as distributed photovoltaics, electric vehicles, and energy storage into distribution networks, the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source, multi-load systems. This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches—primarily focused on economic objectives—insufficient to meet the growing demands for flexible scheduling and dynamic response. To address these challenges, this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational… More >

  • Open Access

    ARTICLE

    A Two-Stage Feature Extraction Approach for Green Energy Consumers in Retail Electricity Markets Using Clustering and TF–IDF Algorithms

    Wei Yang1, Weicong Tan1, Zhijian Zeng1, Ren Li1, Jie Qin1, Yuting Xie1, Yongjun Zhang2, Runting Cheng2, Dongliang Xiao2,*

    Energy Engineering, Vol.122, No.5, pp. 1697-1713, 2025, DOI:10.32604/ee.2025.060571 - 25 April 2025

    Abstract The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies, which poses greater challenges for the market service for green energy consumers. This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and term frequency-inverse document frequency (TF–IDF) algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market. First, the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and… More >

  • Open Access

    ARTICLE

    Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

    Weitao Ha1, Sheng Gang2, Yahya D. Navaei3, Abubakar S. Gezawa4, Yaser A. Nanehkaran2,5,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3025-3057, 2025, DOI:10.32604/cmc.2025.061343 - 16 April 2025

    Abstract Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they face is the “cold start” problem, where new users have no past interactions to guide recommendations. To improve user experience, these systems aim to effectively recommend music even to such users by considering their listening behavior and music popularity. This paper introduces a novel music recommendation system… More >

  • Open Access

    ARTICLE

    Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

    Aadil Mushtaq Pandith1, Manni Kumar2, Naveen Kumar3, Nitin Goyal4,*, Sachin Ahuja2, Yonis Gulzar5, Rashi Rastogi6, Rupesh Gupta7

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 259-279, 2025, DOI:10.32604/cmc.2025.062608 - 26 March 2025

    Abstract Underwater wireless sensor networks (UWSNs) rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink. However, many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments. Additionally, conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink, commonly known as the energy hole issue. Moreover, cluster-based aggregation methods face significant challenges such as cluster head (CH) failures and collisions within clusters that degrade overall network performance. To address these limitations,… More >

  • Open Access

    ARTICLE

    FedCPS: A Dual Optimization Model for Federated Learning Based on Clustering and Personalization Strategy

    Zhen Yang1, Yifan Liu1,2,*, Fan Feng3, Yi Liu3, Zhenpeng Liu1,3

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 357-380, 2025, DOI:10.32604/cmc.2025.060709 - 26 March 2025

    Abstract Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’ devices without sharing private data. It trains a global model through collaboration between clients and the server. However, the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability. Meanwhile, data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks, and standalone personalization tasks may cause severe overfitting issues. To address these limitations, we introduce a federated learning dual optimization model based on… More >

  • Open Access

    ARTICLE

    Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

    Jeng-Shyang Pan1,2,3, Mengfei Zhang1, Shu-Chuan Chu2,*, Xingsi Xue4, Václav Snášel5

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 475-496, 2025, DOI:10.32604/cmc.2025.060170 - 26 March 2025

    Abstract Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis. Traditional clustering algorithms, such as K-means, are widely used due to their simplicity and efficiency. This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm (SPPE) to improve clustering performance. The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution (PPE) algorithm. Firstly, a Variable Neighborhood Search (VNS) factor is incorporated to strengthen the local search capability and foster population diversity. Secondly, a position update model, incorporating a spiral mechanism, is… More >

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