Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applied multivariate techniques
Applied multivariate techniques
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy cluster validation index based on inter-cluster proximity
Pattern Recognition Letters
Stability-based validation of clustering solutions
Neural Computation
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Validity index for clusters of different sizes and densities
Pattern Recognition Letters
A note on ball segment picking related to clustering
Pattern Recognition Letters
An effective evaluation measure for clustering on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral clustering as an automated SOM segmentation tool
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Determining the number of clusters using information entropy for mixed data
Pattern Recognition
An extensive comparative study of cluster validity indices
Pattern Recognition
Proceedings of the Winter Simulation Conference
| Hi-index | 0.10 |
Although the goal of clustering is intuitively compelling and its notion arises in many fields, it is difficult to define a unified approach to address the clustering problem and thus diverse clustering algorithms abound in the research community. These algorithms, under different clustering assumptions, often lead to qualitatively different results. As a consequence the results of clustering algorithms (i.e., data set partitionings) need to be evaluated as regards their validity based on widely accepted criteria. In this paper a cluster validity index, CDbw, is proposed which assesses the compactness and separation of clusters defined by a clustering algorithm. The cluster validity index, given a data set and a set of clustering algorithms, enables (i) the selection of the input parameter values that lead an algorithm to the best possible partitioning of the data set, and (ii) the selection of the algorithm that provides the best partitioning of the data set. CDbw handles efficiently arbitrarily shaped clusters by representing each cluster with a number of points rather than by a single representative point. A full implementation and experimental results confirm the reliability of the validity index showing also that its performance compares favourably to that of several others.