AsadiAhmad/Partition-Based-Clustering
Comparing partition based clustering, K-means, K-means++, K-medoid
This project helps data analysts and researchers quickly compare different clustering methods like K-means, K-means++, and K-medoid. You input your raw data, and it helps you understand how different algorithms group your data, visualizing the distinctions between them. This is useful for anyone trying to segment data without predefined categories.
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Use this if you need to explore and compare how different data points cluster together using popular partition-based methods.
Not ideal if you already know exactly which clustering algorithm you want to use, or if you need to perform more advanced, non-partition-based clustering.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Dec 24, 2024
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