Kernel intuitionistic fuzzy c-means and state transition algorithm for clustering problem
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METHODOLOGIES AND APPLICATION
Kernel intuitionistic fuzzy c-means and state transition algorithm for clustering problem Xiaojun Zhou1,2 · Rundong Zhang1 · Xiangyue Wang1 · Tingwen Huang3 · Chunhua Yang1
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Clustering problems widely exist in machine learning, pattern recognition, image analysis and information sciences, etc. Although many clustering algorithms have been proposed, it is unpractical to find a clustering algorithm suitable for all types of datasets. Fuzzy c-means (FCM) is one of the most frequently-used fuzzy clustering algorithm for the reason that it is efficient, straightforward, and easy to implement. However, the traditional FCM taking Euclidean distance as similarity measurement can not distinguish the intersection between two clusters. Therefore, kernel function has been taken as similarity measurement to solve this issue. As a comprehensive partition criterion, intuitionistic fuzzy set which consider both membership degree and non-membership degree has been used to replace traditional fuzzy set to describe the natural attributes of objective phenomena more delicately. Thus, Kernel intuitionistic fuzzy c-means (KIFCM) has been proposed in this paper to settle clustering problem. Considering FCM is easily getting trapped in local optima due to its high sensitivity to initial centroid. State Transition Algorithm (STA) has been adopted in this study to obtain the initial centroid to enhance its stability. The proposed STA-KIFCM compared with some other clustering algorithms are implemented using five benchmark datasets. Experimental results not only show that the proposed method is efficient and can reveal encouraging results, but also indicate that the proposed method can achieve high accuracy. Keywords Clustering problem · State transition algorithm · Kernel function · Intuitionistic fuzzy set · Fuzzy c-means
1 Introduction Clustering problems exist in many areas including machine learning, pattern recognition, image analysis and information sciences, etc (Sipe 2001; Fouche and Langit 2011; Bastanlar and Ozuysal 2014). With the growing interest in automatically understanding, processing and summarizing data, many application domains have employed various clustering algorithms to identify patterns within a dataset. Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are Communicated by V. Loia.
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Rundong Zhang [email protected]
1
School of Automation, Central South University, Changsha 410083, China
2
The Peng Cheng Laboratory, Shenzhen, Changsha 51800, China
3
Texas A&M University at Qatar, PO Box 23874, Doha, Qatar
more similar to each other than objects from different clusters. Clustering algorithms work by assigning objects to a group if they show a high level of similarity and by assigning objects to different groups if they are distinguished from each other. The common used clustering algorithms can be broadly classified as Hard and F
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