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Developing a hybrid model for comparative analysis of financial data clustering algorithms [PDF]
تصمیم گیری و تحقیق در عملیات, 2023 Purpose: Clustering algorithms are useful tools for understanding data structure and classifying them into different data sets. Due to the importance of using these algorithms in analyzing financial market data that have a high volume and scope, this ...
Mojtaba Movahedi +3 more
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Developed Clustering Algorithms for Engineering Applications: A Review
International Journal of Informatics, Information System and Computer Engineering, 2023 Clustering algorithms play a pivotal role in the field of engineering, offering valuable insights into complex datasets. This review paper explores the landscape of developed clustering algorithms with a focus on their applications in engineering.
Hewa Majeed Zangana, Adnan M Abdulazeez
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A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning [PDF]
PeerJ Computer Science, 2023 Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy ...
Ying Yang, Haoyu Chen, Haoshen Wu
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A Novel Neighborhood Granular Meanshift Clustering Algorithm
Mathematics, 2022 The most popular algorithms used in unsupervised learning are clustering algorithms. Clustering algorithms are used to group samples into a number of classes or clusters based on the distances of the given sample features.
Qiangqiang Chen +5 more
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Multibondic cluster algorithm [PDF]
Nuclear Physics B - Proceedings Supplements, 1995 3 pages, uuencoded compressed postscript file, contribution to the LATTICE'94 ...
Janke, Wolfhard, Kappler, Stefan
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Dynamic evidential clustering algorithm [PDF]
Knowledge-Based Systems, 2021 Abstract In this paper, a dynamic evidential clustering algorithm (DEC) is introduced to address the computational burden of existing methods. To derive such a solution, an FCM-like objective function is first employed and minimized to obtain the support levels of the real singletons (specific) clusters to which the query objects belong, and then the
Zhang, Zuowei +4 more
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Incremental Fuzzy Clustering Based on Feature Reduction
Journal of Electrical and Computer Engineering, 2022 In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions.
Yongli Liu, Yajun Zhang, Hao Chao
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Influence of clustering coefficient on network embedding in link prediction
Applied Network Science, 2022 Multiple network embedding algorithms have been proposed to perform the prediction of missing or future links in complex networks. However, we lack the understanding of how network topology affects their performance, or which algorithms are more likely ...
Omar F. Robledo +3 more
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Comparison of different strategies of utilizing fuzzy clustering in structure identification [PDF]
, 2007 Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification.
Kılıç, Kemal +2 more
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International Journal of Neural Systems, 2008 We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum.
Barbakh, Wesam, Fyfe, Colin
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