K-Means Algorithm In Machine Learning

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K-Means Algorithm In Machine Learning
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Table Of Content

1. What Is a K-Means Algorithm?
2. Why Is the K-Means Algorithm Important?
3. How Does K-Means Algorithm Work?
4. Applications Of K-Means Algorithm

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What Is a K-Means Algorithm?
Unlabeled data can be separated into a specified
number of disjoint sets of similar variance or
clusters using the data clustering technique
K-means for unsupervised machine learning. Due
to its clarity of use and speed on huge
datasets, it is a well-liked algorithm.
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Why Is the K-Means Algorithm Important?
The K-means clustering algorithm is used to
locate groups in the data that have not been
explicitly labeled. This can be used to confirm
organizational hypotheses about the kinds of
existing groups or to find unknown groupings in
large, complex data sets.
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How Does K-Means Algorithm Work?
K-means is a centroid-based clustering algorithm
where per data point is assigned to a cluster
based on the distance between it and a centroid.
Finding the K number of groups in the dataset is
the objective.
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Applications Of K-Means Algorithm

1. Image Reduction
2. Marketing Division
3. Document Grouping
4. Segmenting Customers
5. Trend Analysis With Dynamic Data
6. Segmentation of Images

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