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Clustering algorithm is one of the most popular unsupervised learning algorithms in machine learning. K means clustering is one of the widely used clustering methods for various applications in data mining, image processing and computer vision. Many solutions have been offered to make the k-means clustering algorithm more efficient. This paper proposes an improved k-means clustering algorithm by initializing cluster seeds and improving the time complexity of the algorithm.
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