How many clusters are best?—an experiment
Pattern Recognition
ACM Computing Surveys (CSUR)
Cluster validity methods: part I
ACM SIGMOD Record
Clustering incomplete relational data using the non-Euclidean relational fuzzy c-means algorithm
Pattern Recognition Letters
An Empirical Study on the Visual Cluster Validation Method with Fastmap
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters
Validating and Refining Clusters via Visual Rendering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A method of relational fuzzy clustering based on producing feature vectors using FastMap
Information Sciences: an International Journal
Is VAT really single linkage in disguise?
Annals of Mathematics and Artificial Intelligence
Normality-based validation for crisp clustering
Pattern Recognition
Fuzzy Cluster Validation Using the Partition Negentropy Criterion
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Permutation clustering using the proximity matrix
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A cluster validity index for fuzzy clustering
Fuzzy Sets and Systems
Hi-index | 0.10 |
The assessment of cluster validity plays a very important role in cluster analysis. Most commonly used cluster validity methods are based on statistical hypothesis testing or finding the best clustering scheme by computing a number of different cluster validity indices. A number of visual methods of cluster validity have been produced to display directly the validity of clusters by mapping data into two- or three-dimensional space. However, these methods may lose too much information to correctly estimate the results of clustering algorithms. Although the visual cluster validity (VCV) method of Hathaway and Bezdek can successfully solve this problem, it can only be applied for object data, i.e. feature measurements. There are very few validity methods that can be used to analyze the validity of data where only a similarity or dissimilarity relation exists - relational data. To tackle this problem, this paper presents a relational visual cluster validity (RVCV) method to assess the validity of clustering relational data. This is done by combining the results of the non-Euclidean relational fuzzy c-means (NERFCM) algorithm with a modification of the VCV method to produce a visual representation of cluster validity. RVCV can cluster complete and incomplete relational data and adds to the visual cluster validity theory. Numeric examples using synthetic and real data are presented.