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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
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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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