Interactive interpretation of hierarchical clustering

A new interactive interface is proposed to help the user to interpret the result of such a clustering process, according to the item characteristics, and has been applied successfully to a special case of items providing nice graphical representations.

Abstract

Automatic clustering methods are part of data mining methods. They aim at building clusters of items so that similar items fall into the same cluster while unsimilar items fall into separate clusters. A particular class of clustering methods are hierarchical ones where recursive clusters are formed to grow a binary tree representing an approximation of similarities between items. We propose a new interactive interface to help the user to interpret the result of such a clustering process, according to the item characteristics. The prototype has been applied successfully to a special case of items providing nice graphical representations electric load curves but can also be used with other types of curves or with more standard items.