相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Computer Science, Artificial Intelligence
Yiming Tang et al.
Summary: This study proposes the Density Viewpoint-based Weighted Kernel Fuzzy Clustering (VWKFC) algorithm, which improves the method of introducing domain knowledge into fuzzy clustering. By using the concepts of viewpoints and information granules, new concepts of density radius and weight information granules are introduced. Experimental results show that VWKFC outperforms other clustering algorithms when processing high-dimensional data.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Dongdong Cheng et al.
Summary: The Granular ball (GB) is a coarse-grained representation of data that has been introduced into supervised learning to improve efficiency. In this study, we propose a GB-based DP algorithm (GB-DP) for unsupervised learning. By generating GBs, defining their density and delta-distance, and using them for clustering, GB-DP achieves similar or better clustering results in less running time without setting any parameters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ayush K. Varshney et al.
Summary: Hierarchical clustering using probabilistic intuitionistic fuzzy sets is proposed in this paper to handle data uncertainty. The novel clustering algorithm, termed as Probabilistic Intuitionistic Fuzzy Hierarchical Clustering (PIFHC) Algorithm, utilizes the probabilistic Euclidean distance measure and achieves better cluster accuracies compared to existing counterparts. Experimental results on different datasets demonstrate the effectiveness of the PIFHC algorithm in improving clustering accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Yunlong Gao et al.
Summary: This paper proposes a new robust fuzzy c-means clustering method based on adaptive elastic distance (ARFCM) for image segmentation. By improving the ability to recognize cluster structure, ARFCM can better utilize neighborhood information, improve segmentation accuracy, and achieve clearer textures and more homogeneous regions in images.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fangfang Ye et al.
Summary: This paper proposes an improved convolutional neural network model for image synthesis to enhance synthesis effect and efficiency, and also introduces a low-rank image inpainting method based on a Gaussian mixture model, which maintains local details of the image while describing global low-rank structure.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Atiq Ur Rehman et al.
Summary: This paper introduces a novel non-parametric clustering algorithm based on the concept of divide-and-merge. The algorithm is able to discover both convex and non-convex shaped clusters, handle clusters of different densities, detect and remove outliers/noise, have easily tunable hyperparameters, and be applicable to high dimensional data.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Lianmeng Jiao et al.
Summary: In this study, a transfer learning-based evidential clustering algorithm (TECM) is proposed to address the issue of insufficient or contaminated data on clustering performance. The TECM algorithm integrates knowledge learned from a source domain with the data in a target domain to cluster the target data, demonstrating its effectiveness compared to other representative multitask or transfer-clustering algorithms.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Feiping Nie et al.
Summary: Researchers propose a fast fuzzy clustering algorithm based on anchor graph (FFCAG), which utilizes prior knowledge of anchors to improve clustering performance and reduces computational time.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Tinghui Ouyang et al.
Summary: This paper proposes a DBSCAN extension algorithm with consideration of granule computing to address the scalability and computation cost issues in online structural clustering. The algorithm effectively utilizes the advantages of DBSCAN and granular descriptors, and achieves efficient and effective online clustering by constructing information granules and using rule-based models.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yuqing Yang et al.
Summary: In this paper, a new clustering algorithm named ISBFK-means based on the influence space is proposed to address the issues of huge time overhead and unstable clustering quality when running the K-means algorithm on massive raw data. The approach effectively reduces data volume in the clustering process and improves the stability of clustering quality. Experimental results demonstrate the algorithm's high performance in processing celestial spectral data.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Lun Hu et al.
Summary: This article proposes a fast fuzzy clustering algorithm called F(2)CAN, which incorporates a generalized momentum method into FCAN to address the slow convergence issue, achieving better performance in empirical studies.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Lianmeng Jiao et al.
Summary: This paper introduces an interpretable fuzzy clustering algorithm that combines the flexibility of fuzzy partition with the interpretability of decision tree, using an unsupervised multi-way fuzzy decision tree to achieve interpretability in clustering.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Quang-Thinh Bui et al.
Summary: Topological data analysis is a new theoretical trend that focuses on investigating the global shape of data, with the Mapper algorithm being a representative approach. The introduced Shape Fuzzy C-Means algorithm combines the features of Fuzzy C-Means and Mapper, exhibiting strong clustering ability and the capability to visualize global data shapes.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Shuliang Wang et al.
Summary: Peak clustering, a density based clustering method, has shortcomings in finding cluster centers in low density clusters and detecting normal points as noises. Extreme clustering, which identifies density extreme points and introduces a noise detection module, overcomes these drawbacks and improves performance significantly.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Yan Ma et al.
Summary: The paper introduces a novel hierarchical clustering algorithm based on minimum spanning tree (MST), which uses new ideas in computing inter-cluster distances and merging subclusters. Through three stages of operations, the proposed method shows good performance in clustering based on experimental results.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Kun Song et al.
Summary: BSGP is a popular algorithm for bipartite graph partition, but efficiency is limited due to the use of SVD. To address this, WBKM is proposed as a faster alternative with solutions closer to the ideal. Extensive experiments show WBKM outperforms other methods in computational speed and clustering accuracy.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Yewang Chen et al.
Summary: The article analyzes the drawbacks of DBSCAN and its variants, proposing two techniques for improvement: xi-norm ball and fast approximate algorithm. Additionally, cover tree is used to accelerate density computations, and a method called BLOCK-DBSCAN is introduced for large-scale data.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Xiao Xu et al.
Summary: A novel fast sparse search density peaks clustering (FSDPC) algorithm is proposed to enhance the efficiency of density peaks clustering (DPC) by using a sparse search strategy and a random third-party data point method, which outperforms the DPC and other state-of-the-art algorithms in terms of both computational complexity and clustering performance.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jinghua Wang et al.
Summary: This work introduces an unsupervised deep clustering framework, where adjusting the Gaussian components and data representations provides adaptive support. Experimental results show that this method can improve clustering performance.
Article
Computer Science, Artificial Intelligence
Jie Zhou et al.
Summary: This study introduces a novel locality preserving based fuzzy C-means (LPFCM) clustering method which enhances the capability of handling high-dimensional data by introducing projection techniques and integrating the ideas of fuzzy clustering, geometric structure preservation, and feature extraction. Experimental results demonstrate the effectiveness of LPFCM compared to FCM and some state-of-the-art methods on benchmark data sets.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Qidan Zhu et al.
Summary: The study proposed the K-DBSCAN clustering method, utilizing the novel harmony search (novel-HS) optimization algorithm to improve the clustering parameters of DBSCAN, achieving better clustering results with K classifications. Experimental results demonstrated that this designed clustering method outperforms others and can be considered as a new clustering scheme for further research.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Wenlong Chen et al.
Summary: The traditional dam health evaluation methods overlook the spatiotemporal diversity in deformation behavior. A comprehensive displacement prediction method is proposed, which achieves reliable identification of dam deformation and captures complex mappings through spatiotemporal clustering and machine learning algorithms, demonstrating excellent performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Chunrong Wu et al.
Summary: Hierarchical clustering method HCNN effectively groups similar data by utilizing structural similarities in the nearest neighbor graph, identifying clusters and outliers while reducing the influence of obscure boundaries. The method merges clusters more efficiently by considering equivalence relations based on maximum similarity, leading to improved clustering efficiency and accuracy.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Junyi Guan et al.
Summary: FHC-LDP introduces an association degree transfer method to address the drawbacks of DPC caused by non-adjacent associations, enabling fast identification of local density peaks and automatic generation of sub-clusters. By analyzing the similarity of sub-cluster centers, a hierarchical structure of sub-clusters is built to ensure the most similarity within each cluster. FHC-LDP outperforms traditional clustering algorithms and other variants of DPC in recognizing cluster structure and running speed.
Article
Computer Science, Artificial Intelligence
Mashaan Alshammari et al.
Summary: We proposed an optimized version of k-nearest neighbor graph by keeping data points and reducing the number of edges for computational efficiency. The method utilizes local statistics to maintain the validity of edges and introduces an optional step for automatically selecting the number of clusters.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Shudong Huang et al.
Summary: Clustering aims to divide input data into different groups based on distance or similarity, with k-means being a widely used method. A deep k-means model is proposed in this study to improve clustering performance by extracting deep representations using deep learning techniques.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Yan Ge et al.
Summary: This paper introduces a novel Mixed-Order Spectral Clustering (MOSC) framework to model both second-order and third-order structures simultaneously. By utilizing new methods, MOSC is able to provide better performance when considering mixed-order structures.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Kasun Bandara et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Information Systems
Pattaramon Vuttipittayamongkol et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Information Systems
Gustavo S. Carnivali et al.
INFORMATION SCIENCES
(2020)
Article
Acoustics
Ri Hyon Sun et al.
SPEECH COMMUNICATION
(2020)
Article
Computer Science, Information Systems
Wen-Bo Xie et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Augustine Monney et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Theory & Methods
Zhipeng Gui et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Artificial Intelligence
Kun Zhan et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2019)
Article
Computer Science, Artificial Intelligence
Hongyun Zhang et al.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2019)
Article
Computer Science, Artificial Intelligence
Hailun Xie et al.
APPLIED SOFT COMPUTING
(2019)
Article
Computer Science, Artificial Intelligence
Xiucai Ye et al.
INTELLIGENT DATA ANALYSIS
(2018)
Article
Computer Science, Information Systems
Taufik Sutanto et al.
SOCIAL NETWORK ANALYSIS AND MINING
(2018)
Article
Computer Science, Artificial Intelligence
Jiaming Xu et al.
