Results 11 to 20 of about 2,131,360 (300)
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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Ensemble clustering via heuristic optimisation [PDF]
, 2010 This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityTraditional clustering algorithms have different criteria and biases, and there is no single algorithm that can be the best solution for a wide range of ...
Li, Jian
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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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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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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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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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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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Dealing with non-metric dissimilarities in fuzzy central clustering algorithms [PDF]
, 2008 Clustering is the problem of grouping objects on the basis of a similarity measure among them. Relational clustering methods can be employed when a feature-based representation of the objects is not available, and their description is given in terms of ...
Filippone, Maurizio +2 more
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BinChill: A Metagenomic Binning Ensemble Method
IEEE Access, 2023 The goal of metagenomic binning is to reconstruct genomes from a mixture of DNA sequences into genomic bins, which can be considered a clustering task.
Oliver S. Bak +4 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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