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http://dx.doi.org/10.6109/jkiice.2020.24.1.1

Improved Density-Independent Fuzzy Clustering Using Regularization  

Han, Soowhan (Department of Applied Software Engineering, Dong-eui University)
Heo, Gyeongyong (Department of Electronic Engineering, Dong-eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.1, 2020 , pp. 1-7 More about this Journal
Abstract
Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results.
Keywords
Fuzzy clustering; Euclidean distance; Cluster density; Density-independent clustering; Regularization;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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