fuzzy-c-means

Clustering and Bayes classifiers Matlab

☆樱花仙子☆ 提交于 2019-12-06 21:55:34
问题 So I am at a cross roads on what to do next, I set out to learn and apply some machine learning algorithms on a complicated dataset and I have now done this. My plan from the very beginning was to combine two possible classifiers in an attempt to make a multi-classification system. But here is where I am stuck. I choose a clustering algorithm (Fuzzy C Means) (after learning some sample K-means stuff) and Naive Bayes as the two candidates for the MCS (Multi-Classifier System). I can use both

whats is the difference between “k means” and “fuzzy c means” objective functions?

限于喜欢 提交于 2019-11-29 22:16:24
I am trying to see if the performance of both can be compared based on the objective functions they work on? BTW, the Fuzzy-C-Means (FCM) clustering algorithm is also known as Soft K-Means . The objective functions are virtually identical , the only difference being the introduction of a vector which expresses the percentage of belonging of a given point to each of the clusters. This vector is submitted to a "stiffness" exponent aimed at giving more importance to the stronger connections (and conversely at minimizing the weight of weaker ones); incidently, when the stiffness factor tends

whats is the difference between “k means” and “fuzzy c means” objective functions?

强颜欢笑 提交于 2019-11-28 18:58:05
问题 I am trying to see if the performance of both can be compared based on the objective functions they work on? 回答1: BTW, the Fuzzy-C-Means (FCM) clustering algorithm is also known as Soft K-Means . The objective functions are virtually identical , the only difference being the introduction of a vector which expresses the percentage of belonging of a given point to each of the clusters. This vector is submitted to a "stiffness" exponent aimed at giving more importance to the stronger connections