K Means
Автор: Sadeq El-Fergany
Загружено: 2024-12-10
Просмотров: 121
K-means Algorithm
The K-means algorithm is a popular, straightforward, unsupervised machine-learning technique for clustering data into distinct groups. It works by partitioning a dataset into K clusters, where each data point belongs to the cluster with the nearest mean, the cluster centroid.
How It Works:
Initialization: Choose K initial centroids randomly from the dataset.
Assignment: Assign each data point to the nearest centroid, forming K clusters.
Update: Recalculate the centroids as the mean of all data points in each cluster.
Iteration: Repeat the assignment and update steps until the centroids no longer change significantly.
Applications:
Customer segmentation
Image compression
Anomaly detection
Document clustering
K-means is widely used due to its simplicity and efficiency, making it a valuable tool for data analysis and pattern recognition.
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