Results 31 to 40 of about 3,381,889 (354)
Towards semi-supervised ensemble clustering using a new membership similarity measure
Automatika, 2023 Hierarchical clustering is a common type of clustering in which the dataset is hierarchically divided and represented by a dendrogram. Agglomerative Hierarchical Clustering (AHC) is a common type of hierarchical clustering in which clusters are created ...
Wenjun Li, Ting Li, Musa Mojarad
doaj +1 more source
hdbscan: Hierarchical density based clustering
Journal of Open Source Software, 2017 HDBSCAN: Hierarchical Density-Based Spatial Clustering of Applications with ...
Leland McInnes, John Healy, S. Astels
semanticscholar +1 more source
Belief Hierarchical Clustering [PDF]
, 2014 In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the ...
J. Schubert +6 more
core +5 more sources
Hierarchical Clustering With Hard-Batch Triplet Loss for Person Re-Identification [PDF]
Computer Vision and Pattern Recognition, 2019 For clustering-guided fully unsupervised person reidentification (re-ID) methods, the quality of pseudo labels generated by clustering directly decides the model performance.
Kaiwei Zeng +3 more
semanticscholar +1 more source
Hierarchical growing cell structures: TreeGCS [PDF]
, 2001 We propose a hierarchical clustering algorithm (TreeGCS) based upon the Growing Cell Structure (GCS) neural network of Fritzke. Our algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the ...
Austin, J., Hodge, V.J.
core +1 more source
Merging $K$-means with hierarchical clustering for identifying general-shaped groups [PDF]
, 2017 Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and $K$-means clustering are two approaches but have different strengths and weaknesses.
Ghosh, Arka P. +2 more
core +4 more sources
Journal of King Saud University: Computer and Information Sciences, 2023 Recently, both ensemble clustering and semi-supervised clustering have emerged as important paradigms of traditional clustering. Ensemble clustering seeks to integrate multiple clustering results from different methods or the same methods with different ...
Hui Shi +3 more
doaj +1 more source
Renyi entropy driven hierarchical graph clustering [PDF]
PeerJ Computer Science, 2021 This article explores a graph clustering method that is derived from an information theoretic method that clusters points in ${{\mathbb{R}}^{n}}$Rn relying on Renyi entropy, which involves computing the usual Euclidean distance between these points.
Frédérique Oggier, Anwitaman Datta
doaj +2 more sources
dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering
Bioinform., 2015 Summary: dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing ...
Tal Galili
semanticscholar +1 more source
Hierarchical Multiple Kernel Clustering
AAAI Conference on Artificial Intelligence, 2021 Current multiple kernel clustering algorithms compute a partition with the consensus kernel or graph learned from the pre-specified ones, while the emerging late fusion methods firstly construct multiple partitions from each kernel separately, and then ...
Jiyuan Liu +4 more
semanticscholar +1 more source