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assume that you have a labeled data set . explain how can you use only k-mean clusterign to build a classification model fot this dataset

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K-means clustering is a type of unsupervised learning which uses unlabeled data, or data without clearly defined categories or groupings.

Explain the term K-means clustering?

Data is divided into k clusters using K-means clustering, with the goal of making the clusters closer together and the individual data points more dispersed.

  • The distance between two points determines how similar they are.
  • The distance can be measured in a variety of ways.
  • One of the most often used distance metrics is the distance measure (minkowski distance for p=2).
  • The calculation of the Euclidean separation distance in a two-dimensional space is shown in the image below.
  • The square of a difference between the points' x and y dimensions is used to calculate it.
  • K-means clustering seeks to maximize distances across clusters while minimizing distances within each cluster.

K-means clustering is a sort of unsupervised learning which uses unlabeled data, or data without clearly defined categories or groupings.

Thus this algorithm's goal is to identify groups among the data, where K stands for the set of organizations.

To know more about the K-means clustering, here

https://brainly.com/question/15016224

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The complete question is-

Assume that you have a labeled dataset. Explain how you can use only K-means clustering to build a classification model for this dataset. What can go wrong?

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