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 dissimilar ones fall into separate clusters. A particular class of clustering methods are hierarchical ones where recursive clusters are formed to grow a 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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Boudaillier, E., Hébrail, G. (1997). Interactive interpretation of hierarchical clustering. In: Komorowski, J., Zytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1997. Lecture Notes in Computer Science, vol 1263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63223-9_127
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DOI: https://doi.org/10.1007/3-540-63223-9_127
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