Computer Science
8 years ago

Revolutionizing Medical Data Clustering for Improved Accuracy and Efficiency

COMPARISON OF PURITY AND ENTROPY OF K-MEANS CLUSTERING AND FUZZY C MEANS CLUSTERING
Satya Chaitanya Sripada

Paper Summary

The article compares K-means and Fuzzy C Means clustering using Purity and Entropy on medical data. K-means algorithm assigns objects to clusters based on their proximity to seed points, while Fuzzy C Means algorithm iteratively updates cluster centers based on data points' degrees of belonging. The performance of Fuzzy C Means algorithm depends on the initial centroids chosen.

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