K-Clustering [Biostatistics & Machine Learning]
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K-means clustering is a popular unsupervised machine learning algorithm used for partitioning a dataset into K distinct, non-overlapping clusters. It aims to group similar data points together and to keep dissimilar points apart. This algorithm is widely used in various fields, including data mining, pattern recognition, image segmentation, and more.
Key Concepts of K-means Clustering:
Centroid: Each cluster is represented by a centroid, which is the mean of all data points assigned to that cluster.
Cluster Assignment: Data points are assigned to the cluster with the nearest centroid based on a distance metric.
Advantages of K-means Clustering:
Simplicity: K-means is relatively easy to understand and implement.
Efficiency: It can handle large datasets efficiently.
Scalability: It can scale to a large number of dimensions.
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