Fuzzy C means Clustering #python -- 5
Budget: $10 – $30 USD
Use the data provided in the file “clustering_assignmentData.csv” where we know that there are three clusters from the labels (K = 3). Apply Fuzzy C-means algorithm on the data provied where each row in the file corresponds to one data point.
Important Notes:
One important aspect of fuzzy c means that changes the results significantly is the initialization. So you should run the fuzzy c-means algorithm with the given initialization below above 100 times. The final output of the clustering is the result that gives the minimum J ( the objective function in 1.png). One possible startegy for intialization cluster centers is to select the furthest points from each other. it's required to try clustering with different q (the fuzziness Index and visualize the results). the task is more detailed in the Jupiter nootbook
Important Notes:
One important aspect of fuzzy c means that changes the results significantly is the initialization. So you should run the fuzzy c-means algorithm with the given initialization below above 100 times. The final output of the clustering is the result that gives the minimum J ( the objective function in 1.png). One possible startegy for intialization cluster centers is to select the furthest points from each other. it's required to try clustering with different q (the fuzziness Index and visualize the results). the task is more detailed in the Jupiter nootbook