What is K-Means Clustering?
Among clustering algorithms, K-means is a widely popular and effective method. It partitions data into K distinct clusters, where K is a pre-defined number.
The goal of K-means is to minimize distances within clusters and maximize distances between clusters. This creates internally similar and externally distinct groups. K-means has numerous applications, such as:
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Customer segmentation: grouping customers for targeted marketing;
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Document clustering: organizing documents by topic;
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Image segmentation: dividing images for object recognition;
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Anomaly detection: identifying unusual data points.
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What is K-Means Clustering?
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Among clustering algorithms, K-means is a widely popular and effective method. It partitions data into K distinct clusters, where K is a pre-defined number.
The goal of K-means is to minimize distances within clusters and maximize distances between clusters. This creates internally similar and externally distinct groups. K-means has numerous applications, such as:
-
Customer segmentation: grouping customers for targeted marketing;
-
Document clustering: organizing documents by topic;
-
Image segmentation: dividing images for object recognition;
-
Anomaly detection: identifying unusual data points.
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