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Learn What is K-Means Clustering? | K-Means
Cluster Analysis

bookWhat 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:

  • 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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SectionΒ 3. ChapterΒ 1

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bookWhat 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.

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 1
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