Results 21 to 30 of about 1,195,838 (307)

Hierarchical video summarisation in reference frame subspace [PDF]

, 2009
In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two ...
Sadka, AH, Crookes, D, Jiang, RM
core   +6 more sources

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm [PDF]

, 2013
We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data.
Cooke, Emma J., Wild, David L., Robert Darkins, Emma J Cooke, Darkins Robert, Cooke Emma J., Ghahramani Zoubin, Savage, Richard S., Paul D W Kirk, David L Wild, Ghahramani, Zoubin, Kirk Paul D. W., Zoubin Ghahramani, Richard S Savage, Darkins, Robert, Savage Richard S., Kirk, P. (Paul), Wild David L.   +17 more
core   +1 more source

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

IEEE Access, 2022
Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma.
Naoki Masuyama, Narito Amako, Yuna Yamada, Yusuke Nojima, Hisao Ishibuchi   +4 more
doaj   +1 more source

Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics [PDF]

, 2013
Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper,
Muhammad F Bari, Bari Muhammad F., Sirinukunwattana Korsuk, Bari, Muhammad F., Muhammad F. Bari, Savage, Richard S., David R. J. Snead, David R. J. Snead (475600), Richard S Savage, Richard S. Savage, Rajpoot Nasir M., Muhammad F. Bari (417733), Snead, David R. J., David R J Snead, Richard S. Savage (341629), Nasir M. Rajpoot, Korsuk Sirinukunwattana (475599), Rajpoot, Nasir M., Sirinukunwattana, Korsuk, Korsuk Sirinukunwattana, Snead, David R.J., Snead David R. J., Nasir M. Rajpoot (185555), Nasir M Rajpoot, Savage Richard S.   +24 more
core   +2 more sources

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

Large scale hierarchical clustering of protein sequences [PDF]

, 2005
Krause A, Stoye J, Vingron M. Large scale hierarchical clustering of protein sequences. BMC Bioinformatics. 2005;6(1): 15.Background: Searching a biological sequence database with a query sequence looking for homologues has become a routine operation in ...
Stoye Jens, Krause, Antje, Antje Krause, Vingron, Martin, Vingron Martin, Martin Vingron, Krause, A., Jens Stoye, Krause Antje, Stoye, Jens, Stoye, Jens ; https://orcid.org/, Stoye, J., Vingron, M.   +12 more
core   +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

Order Preserving Hierarchical Clustering

, 2022
Partial orders and directed acyclic graphs are common data structures that arise naturally in numerous applications, and that define order between data points.
Bakkelund, Daniel Rygh
core   +1 more source

Hierarchical Adaptive Clustering [PDF]

Informatica, 2008
This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We propose an adaptive clustering method based on a hierarchical agglomerative approach, Hierarchical Adaptive Clustering (HAC), that adjusts the partitioning into clusters that was
Gabriela Serban, Alina Campan
openaire   +2 more sources

Complementary hierarchical clustering [PDF]

Biostatistics, 2007
When applying hierarchical clustering algorithms to cluster patient samples from microarray data, the clustering patterns generated by most algorithms tend to be dominated by groups of highly differentially expressed genes that have closely related expression patterns.
Gen, Nowak, Robert, Tibshirani
openaire   +2 more sources