KMeans clustering
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K-means clustering is an unsupervised learning algorithm that groups unlabeled data points into a specified number of clusters based on their distances from initial random cluster centroids. It works by first randomly selecting cluster centroids, then assigning each data point to the closest centroid and adjusting the centroid positions iteratively until the clusters are stable or the maximum number of iterations is reached. Choosing the optimal number of clusters is important for accurate clustering results.
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KMeans clustering
- 2. Unsupervised learning is a type of algorithm that learns patterns from unlabeled data. Unsupervised Learning Clustering is a type of unsupervised learning. K Means Clustering K Means clustering grouping the samples based on Euclidean distance.
- 3. How to decide the number of clusters?
- 7. Clustering process will initiate random centroids (number of centroids will be equal to number of clusters). Based on the Euclidean distance between data points and each centroids, the centroid positions and the data belongs to each centroids adjust. This process continuous and finally divide the samples into given number of clusters.