Abstract
As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH) algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.
Highlights
Fuzzy clustering is one of the important research branches in many fields, such as knowledge discovery, image processing, machine learning, and pattern recognition
The Fuzzy C-Means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects, and it is most widely-used among various clustering algorithms
If its initial value is selected improperly, it will converge to a local minimum
Summary
Fuzzy clustering is one of the important research branches in many fields, such as knowledge discovery, image processing, machine learning, and pattern recognition. Fuzzy C-Means (FCM) clustering is one of the most popular and well-recognized clustering methods. This method uses the concept of the geometric closeness of data points in Euclidean space. It allocates these data to different clustering, and the distance between these clusters is determined. If its initial value is selected improperly, it will converge to a local minimum. This drawback limits the FCM algorithm to be used in many applications
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