Investigation on basic k-means clustering algorithm to speed up the running time
The most common way used to cluster a data from a data set is to use k-means clustering algorithm. It categorized as one of the fastest algorithm. In fact, it performs slower once it has to process huge amount of data, because it calculate distance between all data point to every centroids in a data set for all iteration that happened in the whole process, although there are some unnecessary computation. The main contribution of this work is an optimized version of basic k-means clustering algorithm which reduces the execution running time.
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