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Determining the number of clusters in a data set

Determining the number of clusters in a data set, a quantity often labelled k as in the k-means algorithm, is a frequent problem in data clustering, and is a distinct issue from the process of actually solving the clustering problem.

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Overview

Elbow method

X-means clustering

Information criterion approach

Information–theoretic approach

Silhouette method

Cross-validation

Finding number of clusters in text databases

Analyzing the kernel matrix

The gap statistics

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Determining the number of clusters in a data set

Nodes55
Edges54
Triples7
Avg. degree1.96
Density0.036364
Components1

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Important terminology

data clusters number clustering distortion method cluster set k-means algorithm distribution value silhouette displaystyle elbow function jump methods point matrix

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
DBSCANinstance ofOther algorithms0.80text
OPTICS algorithm do not require the specification of this parameterinstance ofOther algorithms0.80text
the Akaike information criterioninstance ofuntil a criterion0.80text
k-means for all values of k between 1instance ofThe strategy of the algorithm is to generate a distortion curve for the input data by running a standard clustering algorithm0.80text
ninstance ofThe strategy of the algorithm is to generate a distortion curve for the input data by running a standard clustering algorithm0.80text
and computing the distortioninstance ofThe strategy of the algorithm is to generate a distortion curve for the input data by running a standard clustering algorithm0.80text
genetic algorithms are useful in determining the number of clusters that gives rise to the largest silhouetteinstance ofOptimization techniques0.80text

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