Results 1 to 10 of about 3,381,889 (354)
Contrastive Hierarchical Clustering [PDF]
ECML/PKDD, 2023 Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the information contained in the flat partition is limited.
Znalezniak, Michał +4 more
openaire +4 more sources
Journal of Classification, 2022 AbstractIn the cluster analysis literature, there are several partitioning (non-hierarchical) methods for clustering multivariate objects based on model estimation. Distinct to these methods is the use of a system of n nested statistical models and the optimization of a loss function to best-fit a clustering model to observed data.
Maurizio Vichi +2 more
openaire +3 more sources
Journal of the ACM, 2019 Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization ...
Brad Boehmke, Brandon Greenwell
semanticscholar +10 more sources
Information Theoretic Hierarchical Clustering [PDF]
Entropy, 2011 Hierarchical clustering has been extensively used in practice, where clusters can be assigned and analyzed simultaneously, especially when estimating the number of clusters is challenging.
Babak Nadjar Araabi +2 more
doaj +3 more sources
International Encyclopedia of Statistical Science, 2011 Clustering is a general problem where, given a certain number of data points along with a way of measuring their similarity or dissimilarity, one has to group them up according to that measure.
Fionn Murtagh
semanticscholar +4 more sources
Selective Inference for Hierarchical Clustering. [PDF]
J Am Stat Assoc, 2020 Classical tests for a difference in means control the Type I error rate when the groups are defined a priori. However, when the groups are instead defined via clustering, then applying a classical test yields an extremely inflated Type I error rate ...
Gao LL, Bien J, Witten D.
europepmc +3 more sources
Hierarchical Clustering via Single and Complete Linkage Using Fully Homomorphic Encryption [PDF]
SensorsHierarchical clustering is a widely used data analysis technique. Typically, tools for this method operate on data in its original, readable form, raising privacy concerns when a clustering task involving sensitive data that must remain confidential is ...
Lynin Sokhonn, Yun-Soo Park, Mun-Kyu Lee
doaj +2 more sources
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
doaj +1 more source
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation [PDF]
Computer Vision and Pattern Recognition, 2021 Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
M. Sarfraz +5 more
semanticscholar +1 more source
Contrastive Multi-view Hyperbolic Hierarchical Clustering [PDF]
International Joint Conference on Artificial Intelligence, 2022 Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important.
Fang-Yu Lin +5 more
semanticscholar +1 more source