TejasV58/Fuzzy-C-means-from-scratch
Simple implementation of Fuzzy C-means algorithm using python. It is used for soft clustering purpose. Visualizing the algorithm step by step with the cluster plots at each step and also the final clusters.
This helps data analysts and researchers organize complex datasets where individual data points might belong to multiple categories simultaneously. You input a dataset, like the Iris flower measurements, and it outputs visual plots showing how data points are grouped into 'soft' clusters. This tool is for anyone who needs to identify nuanced patterns in their data beyond simple, rigid classifications.
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Use this if you need to understand overlapping groups within your data, where an item might partially belong to several different categories, rather than just one.
Not ideal if you require strict, distinct divisions for your data points where each item belongs exclusively to one category.
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Last pushed
May 03, 2022
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