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[HTML][HTML] Applications of machine learning to water resources management: A review of present status and future opportunities
Water is the most valuable natural resource on earth that plays a critical role in the socio-
economic development of humans worldwide. Water is used for various purposes, including …
economic development of humans worldwide. Water is used for various purposes, including …
Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection
Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …
many data mining tasks, especially for processing high-dimensional data such as gene …
Model-based clustering
Clustering is the task of automatically gathering observations into homogeneous groups,
where the number of groups is unknown. Through its basis in a statistical modeling …
where the number of groups is unknown. Through its basis in a statistical modeling …
A survey on feature selection approaches for clustering
The massive growth of data in recent years has led challenges in data mining and machine
learning tasks. One of the major challenges is the selection of relevant features from the …
learning tasks. One of the major challenges is the selection of relevant features from the …
A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective
This paper addresses the problems of data management and analytics for decision-aid by
proposing a new vision of Digital Shadow (DS) which would be considered as the core …
proposing a new vision of Digital Shadow (DS) which would be considered as the core …
[ספר][B] Mixture model-based classification
PD McNicholas - 2016 - taylorfrancis.com
" This is a great overview of the field of model-based clustering and classification by one of
its leading developers. McNicholas provides a resource that I am certain will be used by …
its leading developers. McNicholas provides a resource that I am certain will be used by …
Model-based clustering of high-dimensional data: A review
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 …
Model-based clustering
The notion of defining a cluster as a component in a mixture model was put forth by
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
Finite mixture models
The important role of finite mixture models in the statistical analysis of data is underscored
by the ever-increasing rate at which articles on mixture applications appear in the statistical …
by the ever-increasing rate at which articles on mixture applications appear in the statistical …
A framework for feature selection in clustering
We consider the problem of clustering observations using a potentially large set of features.
One might expect that the true underlying clusters present in the data differ only with respect …
One might expect that the true underlying clusters present in the data differ only with respect …