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'K' means clustering algorithm has been identified to delineate KMA into 'K' number of clusters.
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Simple KMeans: Simple k-means clustering algorithm [20].
But k-means clustering algorithm has the following problems: 1.
A fuzzy k-means clustering algorithm has been adopted.
Reference [23] gives an efficient k-means clustering algorithm.
In this study, a fast and practical K-means clustering algorithm was proposed based on the shortcoming of the traditional K-means clustering algorithm.
The proposal uses a k-means clustering algorithm to index and retrieve fingerprints and palmprints.
A k-means clustering algorithm is employed to partition model data into local surrogate models.
The algorithm utilizes the fuzzy C-means clustering algorithm to partition data before training commences.
To decrease the computational effort, the number of scenarios is reduced using k-means clustering algorithm.
The original k-means clustering algorithm is designed to work primarily on numeric data sets.
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