How many clusters are best?—an experiment
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering
Journal of Global Optimization
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Feature Weighting in k-Means Clustering
Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Stability-based validation of clustering solutions
Neural Computation
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Mining Multidimensional Data Using Clustering Techniques
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
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In this paper we introduce a novel procedure, based on (fuzzy) clustering comparison techniques, to identify association rules between clusters. The procedure we propose is largely based on the use of clustering comparison techniques that we generalized to the fuzzy context. The described methodology can be useful for exploratory data analysis; its complexity is linear to the number of the entities in the data set.