Examples of 'k-means clustering' in a sentence
Meaning of "k-means clustering"
k-means clustering - A method of cluster analysis in data mining, k-means clustering aims to partition n data points into k clusters in which each point belongs to the cluster with the nearest mean
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- A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.
How to use "k-means clustering" in a sentence
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k-means clustering
K-means clustering is an approach for vector quantization.
This is achieved as an iterative approach similar to k-means clustering.
K-means clustering is an unsupervised classification algorithm.
A combined process consisting in k-means clustering and regional vector methodology was proposed.
K-means clustering is also a commonly used algorithm for unsupervised learning.
Such schedules have been known since the work of MacQueen on k-means clustering.
K-means clustering assigns each point to the cluster whose center is nearest.
A comparative study of efficient initialization methods for the k-means clustering algorithm.
Finding the optimal solution to the k-means clustering problem for observations in d dimensions is,.
An alternative to this strategy is the use of clustering algorithms such as k-means clustering.
As a robustly converging alternative to the k-means clustering it is also used for cluster analysis.
The normal process dynamics are then decomposed into different clusters by k-means clustering.
Algorithms like K-means clustering may be employed for this purpose.
Now that the initial centers have been chosen, proceed using standard k-means clustering.
Thus the purpose of K-means clustering is to classify data based on similar expression.
See also
K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm.
K-means clustering is an algorithm for grouping genes or samples based on pattern into K groups.
The analysis is accomplished by performing k-means clustering on three centroids using Euclidean distances.
In the implemented prototype the quantization is carried out by the well known K-means clustering.
A standard clustering technique, k-means clustering with squared Euclidean distance, further confirmed the seasonal trend.
Method according to claim 2 characterized by the use of k-means clustering.
The baseline is composed of K-means clustering algorithms and Markov Models.
The method of claim 2 wherein the clustering comprises k-means clustering.
A prior K-means clustering.
As in Example 4, patients were classified using k-means clustering.
The time series were clustered using k-means clustering with k=100 and the Euclidean distance metric.
Cluster means for factor scores derived from K-means clustering Factor 1.
Hence we switch to K-Means Clustering.
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Examples of using K-means
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K-means has a number of interesting theoretical properties
A key limitation of k-means is its cluster model
K-means clustering is an approach for vector quantization
Examples of using Clustering
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Thematic clustering of questions in the constructive dialogue
Making recommendations on possible clustering or combination of proposals
Clustering activities will not be imposed on projects
