Clustering algorithms: A comparative approach. [PDF]
PLoS ONE, 2019 Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential.
Mayra Z Rodriguez +6 more
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SMART: unique splitting-while-merging framework for gene clustering. [PDF]
PLoS ONE, 2014 Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori.
Rui Fa, David J Roberts, Asoke K Nandi
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Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting [PDF]
Frontiers in Systems Neuroscience, 2020 Deciphering useful information from electrophysiological data recorded from the brain, in-vivo or in-vitro, is dependent on the capability to analyse spike patterns efficiently and accurately.
Rakesh Veerabhadrappa +3 more
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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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Evaluating the effect of beta coefficient on the performance of flexible beta clustering in vegetation classification [PDF]
مجله جنگل ایران, 2022 Among different methods for classification, clustering is commonly used methods. Flexible-Beta clustering is successful hierarchical agglomerative clustering which is employed by ecologists as effective clustering method.
N. Pakgohar +4 more
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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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A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
Applied Sciences, 2023 In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science ...
Shahneela Pitafi +2 more
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HPPD: A Hybrid Parallel Framework of Partition-based and Density-based Clustering Algorithms in Data Streams [PDF]
Al-Rafidain Journal of Computer Sciences and Mathematics, 2020 Data stream clustering refers to the process of grouping continuously arriving new data chunks into continuously changing groups to enable dynamic analysis of segmentation patterns.
Ammar Abd Alazeez
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On fly hybrid swarm optimization algorithms for clustering of streaming data
Results in Control and Optimization, 2023 Clustering is an important data analysis technique for extracting knowledge and hidden patterns in the data. Recently hybrid clustering algorithms have been proposed to solve the local optimum and poor robustness problem due to improper selection of ...
Yashaswini Gowda N., B.R. Lakshmikantha
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Fundamental clustering algorithms suite
SoftwareX, 2021 The article presents immediate access to over fifty fundamental clustering algorithms. Additionally, access to clustering benchmark datasets published priorly as “Fundamental Clustering Problems Suite” (FCPS) is provided.
Michael C. Thrun, Quirin Stier
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