Divisive Hierarchical K-Means

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 . To determine how to divide the nodes in the hierarchical tree into clusters nodes, we use K-means clustering. At each node, to determine the correct number of clusters, we use a quality criterion called Silhouette. The solution that k-means reaches often depends on the starting centroids, however we tested three methods of initialization, and we used the most suitable for our case.