This project uses the K-means clustering algorithm to group data into different clusters.
The K-means algorithm is a widely used unsupervised clustering method in data analysis. It aims to partition a dataset into a certain number of clusters, where each observation belongs to the cluster with the nearest centroid.
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Data Mining: Before applying K-means, it is essential to explore and understand the data to determine the optimal number of clusters.
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K-means implementation: Use the K-means algorithm to cluster data. Make sure you choose the right number of clusters using techniques such as the elbow method.
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Evaluation of results: Evaluate the performance of the K-means classification using metrics such as intra-cluster inertia or silhouette.