Journal ArticleDOI
A K-Means Clustering Algorithm
J. A. Hartigan,M. A. Wong +1 more
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This article is published in Journal of The Royal Statistical Society Series C-applied Statistics.The article was published on 1979-03-01. It has received 10702 citations till now. The article focuses on the topics: Canopy clustering algorithm & Correlation clustering.read more
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Spatial and spectral correlations in MALDI mass spectrometry images by clustering and multivariate analysis.
TL;DR: Principal component and discriminant analyses are combined to comprehensively identify changes in the mass spectra between regions, and clustering methods are used to classify pixels by spectral similarity, facilitating definition of distinct spatial regions.
Proceedings ArticleDOI
Interrelated two-way clustering: an unsupervised approach for gene expression data analysis
TL;DR: A new framework for unsupervised analysis of gene expression data is presented which applies an interrelated two-way clustering approach to the gene expression matrices to find important gene patterns and perform cluster discovery on samples.
Journal ArticleDOI
Automatic clustering using nature-inspired metaheuristics
TL;DR: An up-to-date review of all major nature-inspired metaheuristic algorithms used thus far for automatic clustering, with a strong tendency in using multiobjective and hybrid algorithms to address non-linearly separable problems.
Journal ArticleDOI
A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation
TL;DR: This work proposes a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles.
Journal ArticleDOI
Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
TL;DR: The group-structured prior information of hyperspectral images is incorporated into the nonnegative matrix factorization optimization, where the data are organized into spatial groups to exploit the shared sparse pattern and to avoid the loss of spatial details within a spatial group.
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