An overview of clustering methods
MGH Omran, AP Engelbrecht… - Intelligent Data …, 2007 - content.iospress.com
Data clustering is the process of identifying natural groupings or clusters within
multidimensional data based on some similarity measure. Clustering is a fundamental …
multidimensional data based on some similarity measure. Clustering is a fundamental …
[PDF][PDF] Unsupervised and semi-supervised clustering: a brief survey
N Grira, M Crucianu, N Boujemaa - A review of machine learning …, 2004 - deptinfo.cnam.fr
Clustering (or cluster analysis) aims to organize a collection of data items into clusters, such
that items within a cluster are more “similar” to each other than they are to items in the other …
that items within a cluster are more “similar” to each other than they are to items in the other …
[图书][B] Computational intelligence
Computational Intelligence comprises concepts, paradigms, algorithms, and
implementations of systems that are supposed to exhibit intelligent behavior in complex …
implementations of systems that are supposed to exhibit intelligent behavior in complex …
[图书][B] Pattern recognition
S Theodoridis, K Koutroumbas - 2006 - books.google.com
Pattern recognition is a fast growing area with applications in a widely diverse number of
fields such as communications engineering, bioinformatics, data mining, content-based …
fields such as communications engineering, bioinformatics, data mining, content-based …
[图书][B] Pattern recognition and image preprocessing
ST Bow - 2002 - taylorfrancis.com
Describing non-parametric and parametric theoretic classification and the training of
discriminant functions, this second edition includes new and expanded sections on neural …
discriminant functions, this second edition includes new and expanded sections on neural …
Low-complexity non-intrusive load monitoring using unsupervised learning and generalized appliance models
Awareness of electric energy usage has both societal and economic benefits, which include
reduced energy bills and stress on non-renewable energy sources. In recent years, there …
reduced energy bills and stress on non-renewable energy sources. In recent years, there …
A robust competitive clustering algorithm with applications in computer vision
H Frigui, R Krishnapuram - Ieee transactions on pattern …, 1999 - ieeexplore.ieee.org
This paper addresses three major issues associated with conventional partitional clustering,
namely, sensitivity to initialization, difficulty in determining the number of clusters, and …
namely, sensitivity to initialization, difficulty in determining the number of clusters, and …
[图书][B] Knowledge-based clustering: from data to information granules
W Pedrycz - 2005 - books.google.com
A comprehensive coverage of emerging and current technology dealing with heterogeneous
sources of information, including data, design hints, reinforcement signals from external …
sources of information, including data, design hints, reinforcement signals from external …
A new cluster validity measure and its application to image compression
Many validity measures have been proposed for evaluating clustering results. Most of these
popular validity measures do not work well for clusters with different densities and/or sizes …
popular validity measures do not work well for clusters with different densities and/or sizes …
Agglomerative fuzzy k-means clustering algorithm with selection of number of clusters
In this paper, we present an agglomerative fuzzy k-means clustering algorithm for numerical
data, an extension to the standard fuzzy k-means algorithm by introducing a penalty term to …
data, an extension to the standard fuzzy k-means algorithm by introducing a penalty term to …