Abstract
Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. in this method.The number of clusters is predefined and the technique is highly dependent off the initial identification of elements that represent the clusters well. As the dataset’s scale increases rapidly, it is difficult to use K-means and deal with massive data. partitions.To prevent this problem,refining initial points algorithm provided.it can reduce execution time and improve solutions for large data by setting the refinement of initial conditions.The experiments demonstrate that sample-based K-means is more stable and more accurate.
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