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Unsupervised K-Means Clustering Algorithm
IEEE Access, 2020 The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the
Kristina P. Sinaga, Miin-Shen Yang
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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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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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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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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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Algorithmic clustering of music [PDF]
Proceedings of the Fourth International Conference onWeb Delivering of Music, 2004. EDELMUSIC 2004., 2004 We present a fully automatic method for music classification, based only on compression of strings that represent the music pieces. The method uses no background knowledge about music whatsoever: it is completely general and can, without change, be used in different areas like linguistic classification and genomics.
Cilibrasi, R. +2 more
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Российский технологический журнал, 2020 The genetic algorithm of clustering of analysis objects in different data domains has been offered within the hybrid concept of intelligent information technologies development aimed to support decision-making.
M. A. Anfyorov
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