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Survey on Hierarchical Clustering for Machine Learning [PDF]
Jisuanji kexue, 2023 Clustering analysis plays a key role in machine learning,data mining and biological DNA information.Clustering algorithms can be categorized into flat clustering and hierarchical clustering.Flat clustering mostly divides the data set into K parallel ...
WANG Shaojiang, LIU Jia, ZHENG Feng, PAN Yicheng
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Clustering Algorithm Based on Density of Data [PDF]
E3S Web of Conferences, 2021 The k_means clustering algorithm has very extensive application. The paper gives out_in clustering algorithm based on density. The algorithm combines distance with data density to adapt to data distribution.
Ma Yong
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Time Series Clustering based on Aggregation and Selection of Extracted Features [PDF]
Journal of Artificial Intelligence and Data Mining, 2023 In time series clustering, features are typically extracted from the time series data and used for clustering instead of directly clustering the data. However, using the same set of features for all data sets may not be effective.
Ali Ghorbanian, Hamideh Razavi
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k-medianoids Clustering Algorithm
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 One of the simplest and popular clustering method is the simple k-means clustering algorithm. One of the drawbacks of the method is its sensitivity to outliers. To overcome this problem, the k-medians clustering algorithm is used.
James Cha, Teryn Cha, Sung-Hyuk Cha
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Adaptive Correlation Fusion Clustering Algorithm Based on Natural Neighbor [PDF]
Jisuanji gongcheng, 2020 Most traditional clustering algorithms need to pre-set clustering parameters and fail to recognize outliers and noise.To address the problem,this paper proposes an adaptive correlation fusion clustering algorithm.The algorithm uses the narual neighbor ...
LI Ping, GONG Xiaofeng, LUO Ruisen
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Evaluating Clustering Algorithms: An Analysis using the EDAS Method [PDF]
E3S Web of Conferences, 2023 Data clustering is frequently utilized in the early stages of analyzing big data. It enables the examination of massive datasets encompassing diverse types of data, with the aim of revealing undiscovered correlations, concealed patterns, and other ...
Siva Shankar S. +3 more
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Piecemeal Clustering: a Self-Driven Data Clustering Algorithm
IEEE Access, 2022 Various approaches have been discussed in the literature for the clustering of data, such as partitioning, hierarchical, and machine learning methods.
Md. Monjur Ul Hasan +4 more
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IEEE Access, 2021 In order to improve the global search ability of K-means algorithm and the clustering effect, a K-means method based on the approximate backbone and the shuffled frog leaping algorithm was proposed.
Weiping Ding +3 more
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An Improved Three-Way Clustering Based on Ensemble Strategy
Mathematics, 2022 As a powerful data analysis technique, clustering plays an important role in data mining. Traditional hard clustering uses one set with a crisp boundary to represent a cluster, which cannot solve the problem of inaccurate decision-making caused by ...
Tingfeng Wu, Jiachen Fan, Pingxin Wang
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Study on Affinity Propagation Clustering Algorithm Based on Bacterial Flora Optimization [PDF]
Jisuanji kexue, 2022 In order to improve the clustering performance of the nearest neighbor propagation clustering algorithm,the flora algorithm is used to optimize the parameters of the nearest neighbor propagation bias.Firstly,the similarity matrix is established according
ZHANG Yu-jiao, HUANG Rui, ZHANG Fu-quan, SUI Dong, ZHANG Hu
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