A Dynamic Cluster Algorithm Based on Lr Distances for Quantitative Data

Dynamic clustering methods aim to obtain both a single partition of the input data into a fixed number of clusters and the identification of a suitable representation of each cluster simultaneously. In its adaptive version, at each iteration of these algorithms there is a different distance for the comparison of each cluster with its representation. In this paper, we present a dynamic cluster method based on L r distances for quantitative data.