On the meaning of Dunn's partition coefficient for fuzzy clusters
Fuzzy Sets and Systems
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster validity based on the hard tendency of the fuzzy classification
Pattern Recognition Letters
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach for measuring the validity of the fuzzy c-means algorithm
Advances in Engineering Software
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Unsupervised possibilistic clustering
Pattern Recognition
A Clustering Performance Measure Based on Fuzzy Set Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Pattern Recognition
MiniMax ε-stable cluster validity index for Type-2 fuzziness
Information Sciences: an International Journal
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
Hi-index | 0.01 |
Cluster validity indexes can be used to evaluate the fitness of data partitions produced by a clustering algorithm. Validity indexes are usually independent of clustering algorithms. However, the values of validity indexes may be heavily influenced by noise and outliers. These noise and outliers may not influence the results from clustering algorithms, but they may affect the values of validity indexes. In the literature, there is little discussion about the robustness of cluster validity indexes. In this paper, we analyze the robustness of a validity index using the @f function of M-estimate and then propose several robust-type validity indexes. Firstly, we discuss the validity measure on a single data point and focus on those validity indexes that can be categorized as the mean type of validity indexes. We then propose median-type validity indexes that are robust to noise and outliers. Comparative examples with numerical and real data sets show that the proposed median-type validity indexes work better than the mean-type validity indexes.