Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
ACM Computing Surveys (CSUR)
Feature-based fuzzy classification for interpretation of mammograms
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
Partially supervised clustering for image segmentation
Pattern Recognition
Fuzzy C-means based clustering for linearly and nonlinearly separable data
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Fuzzy c-means (FCM) is one of the most popular techniques for data clustering. Since FCM tends to balance the number of data points in each cluster, centers of smaller clusters are forced to drift to larger adjacent clusters. For datasets with unbalanced clusters, the partition results of FCM are usually unsatisfactory. Cluster size insensitive FCM (csiFCM) dealt with ''cluster-size sensitivity'' problem by dynamically adjusting the condition value for the membership of each data point based on cluster size after the defuzzification step in each iterative cycle. However, the performance of csiFCM is sensitive to both the initial positions of cluster centers and the ''distance'' between adjacent clusters. In this paper, we present a cluster size insensitive integrity-based FCM method called siibFCM to improve the deficiency of csiFCM. The siibFCM method can determine the membership contribution of every data point to each individual cluster by considering cluster's integrity, which is a combination of compactness and purity. ''Compactness'' represents the distribution of data points within a cluster while ''purity'' represents how far a cluster is away from its adjacent cluster. We tested our siibFCM method and compared with the traditional FCM and csiFCM methods extensively by using artificially generated datasets with different shapes and data distributions, synthetic images, real images, and Escherichia coli dataset. Experimental results showed that the performance of siibFCM is superior to both traditional FCM and csiFCM in terms of the tolerance for ''distance'' between adjacent clusters and the flexibility of selecting initial cluster centers when dealing with datasets with unbalanced clusters.