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The k-means algorithm is a simple, clustering algorithm popular for general use [28].
Simple clustering techniques are among the most widely adopted methods to approach the classification problem (Everitt et al. 2011).
Simple clustering methods such as agglomerative hierarchical clustering and k-means have been widely used on gene expression data analysis.
Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data.
K-means clustering is one the fastest and simplest clustering analysis tools that can reduce large datasets into smaller, more manageable subspaces based on the similarities observed in the dataset.
One specimen of so-called proto-feathers had a single bristlelike filament and some simple clusters.
One of the most popular and simple clustering algorithms, K-means, was first published in 1955.
The results are then clustered by a very simple clustering algorithm: All conformations are sorted by order of increasing energy.
A simple clustering scheme is to assign all the frames on the same prediction level to the same cluster.
A most simple clustering algorithm is an exhaustive search of all possible clustering patterns (exhaustive algorithm) [20].
A simple cluster randomized control trial design was used to evaluate a social marketing program, Game On: Know Alcohol (GOKA).
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