An empirical analysis of software effort estimation with outlier elimination
Proceedings of the 4th international workshop on Predictor models in software engineering
Optimizing visual vocabularies using soft assignment entropies
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Hierarchical K-means clustering algorithm based on silhouette and entropy
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
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This paper focuses on clustering methods for content-based image retrieval CBIR. Hierarchical clustering methods are a way to investigate grouping in data, simultaneously over a variety of scales, by creating a cluster tree. Traditionally, these methods group the objects into a binary hierarchical cluster tree. Our main contribution is the proposal of a new divisive hierarchy that is based on the construction of a non-binary tree. Each node can have more than two divisive clusters by detecting a better grouping in m classes (m赂[2,5]). To determine how to divide the nodes in the hierarchical tree into clusters nodes, we use K-means clustering, [1]. At each node, to determine the correct number of clusters, we use a quality criterion called Silhouette. The solution that kmeans reaches often depends on the starting centroids, however we tested three methods of initialization, and we used the most suitable for our case.