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 …
foundations and its flexibility. However, high-dimensional data are nowadays more and …
[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 …
and developing intuition into today's most popular machine learning methods. This book …
Machine learning based workload prediction in cloud computing
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 …
datacenters. It is important for cloud service providers (CSPs) to provide cloud service …
Supporting clustering with contrastive learning
Unsupervised clustering aims at discovering the semantic categories of data according to
some distance measured in the representation space. However, different categories often …
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
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 …
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
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 …
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 …
data. For most applications, applying clustering is only appropriate when cluster structure is …
[图书][B] Data clustering: theory, algorithms, and applications
The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …
2007. Starting with the common ground and knowledge for data clustering, the monograph …
[图书][B] Multivariate kernel smoothing and its applications
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 …
methodology in the data science tool kit for the 21st century. Its widespread adoption is due …