A New Gaussian Kernel FCM Technique for High-Dimensional Information in Real Datasets
Keywords:
Fuzzy c-means method, Gaussian kernel FCM (FCM-GM), ClusteringAbstract
Fuzzy C-Means (FCMs) is a well-known unsupervised partitioning algorithm that is utilized in a variety of applications, including pattern recognition, machine learning, & data mining. The membership values considering each individual computed during each of the clusters cannot reflect how effectively the individuals are categorized, despite FCM's strong performance in discovering clusters. FCM is the most well-known fuzzy clustering algorithm. Although FCM has done a good job regarding detecting clusters, membership values before each element allocated toward each cluster cannot tell us how well the individuals are clustered regarding each variable. A variant of the FCM algorithm for multidimensional data has been developed to solve this problem. The proposed method tells us that variables are not correlated & the data, as well as their weighted counterparts, are linearly separable. This hypothesis ignores the fact that each variable has a varied relevant weight, which may differ from one cluster to the next. In this paper, we present two multivariate FCM algorithms for multidimensional data among weighting. Weights are used to express the relative importance of each variable considering each cluster and to improve clustering quality. Experiments on synthetic and actual data sets reveal that the suggested method generates good clustering quality.
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