k-Means Algorithm Implementation with Custom Data Samples and Cluster Centers
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This article discusses the k-means algorithm, a widely-used clustering technique that operates on user-defined data samples and initial cluster centers. The algorithm employs an iterative approach where cluster centers are locally updated through reassignment of data points to their nearest centroids. Key implementation aspects include: the Euclidean distance calculation for measuring similarity between data points, centroid recalculation by averaging all points within each cluster, and convergence checking when cluster assignments stabilize. We are pleased to announce that this implementation has been thoroughly debugged and includes clear visualizations that effectively demonstrate the algorithm's working mechanism. The k-means algorithm finds applications across diverse domains including data mining (for customer segmentation), image processing (for color quantization), and natural language processing (for document categorization).
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