Fuzzy C-Means Algorithm Automatically Determining Optimal Number of Clusters

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the...

Automatically Determining Number of Clusters

Automatically determining number of clusters in the data is an unsolved/unexplored problem. First I'll show why we need to do this, and whether this is a reasonable problem in text clustering in particular. Then starting from simple 1-d/2-d study, I nd BIC (Bayesian Information Criterion) is a useful measure, which by penalizing model tness by model complexity, usually tells the right number of...

An Expansion of -means for Automatically Determining the Optimal Number of Clusters

We expand a non-hierarchical clustering algorithm that can determine the optimal number of clusters by using iterations of -means and a stopping rule based on Bayesian Information Criterion (BIC). The procedure requires merging the clusters that a -means iteration has made to avoid unsuitable division caused by the division order. By using this additional merging operation, the case of adequate...

Fault Diagnosis Based on Fuzzy C-means Algorithm of the Optimal Number of Clusters and Probabilistic Neural Network

Fault diagnosis is essential for the reliable, safe, and efficient operation of the plant and for maintaining quality of the products in industrial system. This paper presents an ensemble fault diagnosis algorithm based on fuzzy c-means algorithm (FCM) with the Optimal Number of Clusters (ONC) and probabilistic neural network (PNN), called FCM-ONC-PNN. In clustering methods, the estimation of t...

OptCluster : an R package for determining the optimal clustering algorithm and optimal number of clusters