OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Machine Learning
Interactive Interpretation of Hierarchical Clustering
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Automatic extraction of clusters from hierarchical clustering representations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
An AI tool for the petroleum industry based on image analysis and hierarchical clustering
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Clustering and classification techniques for blind predictions of reservoir facies
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Unsupervised and supervised learning in cascade for petroleum geology
Expert Systems with Applications: An International Journal
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Clustering is the task of categorizing objects into different classes in an unsupervised way. Hierarchical clustering algorithms are usually very effective in detecting the dataset underlying structure. However, they do not create clusters, but compute only a hierarchical representation of the dataset. It is then desirable to define an automatic technique for cluster creation in hiearchical clustering algorithms. To this purpose, in this paper we present an algorithm that finds the best clustering partition according to clustering validity indexes. In particular, our automatic approach performs a validity index-driven search through a clustering tree. The best partition is then selected cutting the tree in a non-horizontal way. The algorithm was implemented in a software tool and then tested on different datasets. The overall system makes then hierarchical clustering an automatic step, where no user interaction is needed in order to select clusters from a hierarchical cluster representation.