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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
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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
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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
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Bioengineering, 2022 Heart failure with preserved ejection (HFpEF) is a heterogenous condition affecting nearly half of all patients with heart failure (HF). Artificial intelligence methodologies can be useful to identify patient subclassifications with important clinical ...
Hirmand Nouraei +2 more
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Convex clustering: an attractive alternative to hierarchical clustering.
PLoS Computational Biology, 2015 The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims.
Gary K Chen +3 more
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Temporal Hierarchical Clustering
CoRR, 2017 14 pages, 4 ...
Dey, Tamal K. +2 more
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RECURSIVE HIERARCHICAL CLUSTERING FOR HYPERSPECTRAL IMAGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Partition based clustering techniques are widely used in data mining and also to analyze hyperspectral images. Unsupervised clustering only depends on data, without any external knowledge.
S. May
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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 belief function framework.
Wiem Maalel +3 more
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Neutrosophic Hierarchical Clustering Algoritms [PDF]
Neutrosophic Sets and Systems, 2014 Interval neutrosophic set (INS) is a generalization of interval valued intuitionistic fuzzy set (IVIFS), whose the membership and non-membership values of elements consist of fuzzy range, while single valued neutrosophic set (SVNS) is regarded as ...
Rıdvan Şahin
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A Data-Driven Clustering Recommendation Method for Single-Cell RNA-Sequencing Data
Tsinghua Science and Technology, 2021 Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it possible to solve biological problems at the single-cell resolution.
Yu Tian +5 more
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