Model-based clustering of high-dimensional data: A review

C Bouveyron, C Brunet-Saumard - Computational Statistics & Data Analysis, 2014 - Elsevier
Abstract Model-based clustering is a popular tool which is renowned for its probabilistic
foundations and its flexibility. However, high-dimensional data are nowadays more and …

Astronomical image and data analysis

JL Starck, F Murtagh - 2007 - books.google.com
With information and scale as central themes, this comprehensive survey explains how to
handle real problems in astronomical data analysis using a modern arsenal of powerful …

[图书][B] Hands-on machine learning with R

B Boehmke, BM Greenwell - 2019 - taylorfrancis.com
Hands-on Machine Learning with R provides a practical and applied approach to learning
and developing intuition into today's most popular machine learning methods. This book …

Machine learning based workload prediction in cloud computing

J Gao, H Wang, H Shen - 2020 29th international conference …, 2020 - ieeexplore.ieee.org
As a widely used IT service, more and more companies shift their services to cloud
datacenters. It is important for cloud service providers (CSPs) to provide cloud service …

Supporting clustering with contrastive learning

D Zhang, F Nan, X Wei, S Li, H Zhu, K McKeown… - arXiv preprint arXiv …, 2021 - arxiv.org
Unsupervised clustering aims at discovering the semantic categories of data according to
some distance measured in the representation space. However, different categories often …

[HTML][HTML] mclust 5: clustering, classification and density estimation using Gaussian finite mixture models

L Scrucca, M Fop, TB Murphy, AE Raftery - The R journal, 2016 - ncbi.nlm.nih.gov
Finite mixture models are being used increasingly to model a wide variety of random
phenomena for clustering, classification and density estimation. mclust is a powerful and …

[图书][B] Model-based clustering and classification for data science: with applications in R

C Bouveyron, G Celeux, TB Murphy, AE Raftery - 2019 - books.google.com
Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

To cluster, or not to cluster: An analysis of clusterability methods

A Adolfsson, M Ackerman, NC Brownstein - Pattern Recognition, 2019 - Elsevier
Clustering is an essential data mining tool that aims to discover inherent cluster structure in
data. For most applications, applying clustering is only appropriate when cluster structure is …

[图书][B] Data clustering: theory, algorithms, and applications

G Gan, C Ma, J Wu - 2020 - SIAM
The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

[图书][B] Multivariate kernel smoothing and its applications

JE Chacón, T Duong - 2018 - taylorfrancis.com
Kernel smoothing has greatly evolved since its inception to become an essential
methodology in the data science tool kit for the 21st century. Its widespread adoption is due …