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ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2021
Thomas B. Fomby
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Thomas B. Fomby
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Cluster analysis: A modern statistical review
WIREs Computational Statistics, 2022Cluster analysis is a big, sprawling field. This review paper cannot hope to fully survey the territory. Instead, it focuses on hierarchical agglomerative clustering, k‐means clustering, mixture models, and then several related topics of which any ...
Adam Jaeger, David Banks
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Performance Measurement and Metrics, 2021
PurposeThis literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science ...
Brady D. Lund, Jinxuan Ma
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PurposeThis literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science ...
Brady D. Lund, Jinxuan Ma
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Veterinary Immunology and Immunopathology, 1996
Cluster analysis was performed on flow cytometry data generated from the reactivities of the 302 workshop monoclonal antibodies with 36 target cell preparations. The antibodies were assigned to 42 preliminary clusters that were subjected to further examination in subsequent stages of the workshop.
A. A. Afifi, V. Clark
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Cluster analysis was performed on flow cytometry data generated from the reactivities of the 302 workshop monoclonal antibodies with 36 target cell preparations. The antibodies were assigned to 42 preliminary clusters that were subjected to further examination in subsequent stages of the workshop.
A. A. Afifi, V. Clark
openaire +3 more sources
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980
Cluster analysis is concerned with the problem of partitioning a given set of entities into homogeneous and well-separated subsets called clusters. The concepts of homogeneity and of separation can be made precise when a measure of dissimilarity between the entities is given.
Delattre, Michel, Hansen, Pierre
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Cluster analysis is concerned with the problem of partitioning a given set of entities into homogeneous and well-separated subsets called clusters. The concepts of homogeneity and of separation can be made precise when a measure of dissimilarity between the entities is given.
Delattre, Michel, Hansen, Pierre
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Comprehensive cluster analysis with Transitivity Clustering
Nature Protocols, 2011Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a ...
Wittkop, T. +5 more
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Multilevel Functional Clustering Analysis
Biometrics, 2012Summary In this article, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g., genes) at multiple subunits (e.g., bacteria types). To describe the within‐ and between variability induced by the hierarchical structure in the data, we take a multilevel ...
Serban, Nicoleta, Jiang, Huijing
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