A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity measure between fuzzy sets and between elements
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
Fuzzy cluster validation index based on inter-cluster proximity
Pattern Recognition Letters
A cluster validation index for GK cluster analysis based on relative degree of sharing
Information Sciences—Informatics and Computer Science: An International Journal
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
New indices for cluster validity assessment
Pattern Recognition Letters
An objective approach to cluster validation
Pattern Recognition Letters
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Relational visual cluster validity (RVCV)
Pattern Recognition Letters
A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A validity criterion for fuzzy clustering
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
FUAT - A fuzzy clustering analysis tool
Expert Systems with Applications: An International Journal
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In this paper, a cluster validity index proposed by Kim et al. [15] is analyzed, and a problem is discussed that the validity index faces in situations when there are well-separated clusters that themselves include subclusters. Based on this analysis, a new validity index is proposed. The new validity index employs a compactness measure and a separation measure. The compactness measure combines the fuzziness in the membership matrix (U) with the geometrical compactness of the representation of the data set (X) via the prototypes (V). The separation measure is defined as the average value of the degrees of overlap of all possible pairs of fuzzy clusters in the system. The proposed index is tested and validated using several data sets. The results of the comparison show the superior effectiveness and reliability of the proposed index in comparison to other indices.