Results 261 to 270 of about 636,655 (299)

Stability-Based Validation of Clustering Solutions

Neural Computation, 2004
Data clustering describes a set of frequently employed techniques in exploratory data analysis to extract “natural” group structure in data. Such groupings need to be validated to separate the signal in the data from spurious structure. In this context, finding an appropriate number of clusters is a particularly important model selection question.
Lange, Tilman, Roth, Volker, Braun, Mikio L., Buhmann, Joachim M.   +3 more
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Cluster Validity with Fuzzy Sets

Journal of Cybernetics, 1973
Abstract Given a finite, unlabelled set of real vectors X, one often presumes the existence of (c) subsets (clusters) in X, the members of which somehow bear more similarity to each other than to members of adjoining clusters. In this paper, we use membership function matrices associated with fuzzy c-partitions of X, together with their values in the ...
openaire   +1 more source

Clustering validity checking methods

ACM SIGMOD Record, 2002
Clustering results validation is an important topic in the context of pattern recognition. We review approaches and systems in this context. In the first part of this paper we presented clustering validity checking approaches based on internal and external criteria.
Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis   +2 more
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Cluster validity for fuzzy clustering algorithms

Fuzzy Sets and Systems, 1981
Abstract The proportion exponent is introduced as a measure of the validity of the clustering obtained for a data set using a fuzzy clustering algorithm. It is assumed that the output of an algorithm includes a fuzzy nembership function for each data point.
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Cluster validity for noise aware clusterings

Intelligent Data Analysis: An International Journal
There are clustering algorithms (such as DBSCAN) that do not group all data into clusters, but identify some data as noise and exclude it from clusters. In the literature there are no dedicated validity measures for this kind of noise-aware clusterings.
Lea Eileen Brauner, Frank Höppner, Frank Klawonn   +2 more
openaire   +1 more source

An Updated Guideline for Assessing Discriminant Validity

Organizational Research Methods, 2022
Mikko Rönkkö, Eunseong Cho
exaly  

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Nature Reviews Drug Discovery, 2022
, John Hickman, Gerard Dawson
exaly  

Cluster Validation

2015
Oleg Granichin, Zeev Volkovich, Dvora Toledano-Kitai   +2 more
openaire   +1 more source

Clustering Validation

2020
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A new criterion for assessing discriminant validity in variance-based structural equation modeling

Journal of the Academy of Marketing Science, 2014
Jörg Henseler, Christian M Ringle, Marko Sarstedt   +2 more
exaly