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Ensemble methods for clustering take a collection of input partitions, produced for the same data set, and generate an ensemble partition that tries to preserve the information carried in this collective. Acceptance of the resulting partition(s) by decision makers can be a problem, due to the inherent complexity of ensemble techniques, and the associated lack of intuition on how a consensus has been...
Recent advances in cluster analysis highlight the importance of finding multiple meaningful partitions and point out to the need for approaches to evaluate them. They also suggest that the evaluation should consider knowledge of a domain expert. In this paper, we present a visualization method, called PVis1 (Partition's Visualizer), that allows the integrated visualization of a collection of partitions...
The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public...
For highly imbalanced data sets, almost all the instances are labeled as one class, whereas far fewer examples are labeled as the other classes. In this paper, we present an empirical comparison of seven different clustering evaluation indices when used to assess partitions generated from highly imbalanced data sets. Some of the metrics are based on matching of sets (F-measure), information theory...
No clustering algorithm is guaranteed to find actual groups in any dataset. Thus, the selection of the most suitable clustering algorithm to be applied to a given dataset is not easy. To deal with this problem, one can apply various clustering algorithms to the dataset, generating a set of partitions (solutions). Next, one can choose the best partition generated, according to a given validation measure...
Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK—Multi-Objective Clustering with automatic K-determination and MOCLE—Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number...
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