In this work, we present an instantiation of our framework for Hierarchical Distance-based Conceptual Clustering (HDCC) using sequences, a particular kind of structured data. We analyze the relationship between distances and generalization operators for sequences in the context of HDCC. HDCC is a general approach to conceptual clustering that extends the traditional algorithm for hierarchical clustering by producing conceptual generalizations of the discovered clusters. Since the approach is general, it allows combining the flexibility of changing distances for different data types at the same time that we take advantage of the interpretability offered by the obtained concepts, which is central for descriptive data mining tasks. We propose here different generalization operators for sequences and analyze how they work together with the edit and linkage distances in HDCC. This analysis is carried out based on three different properties for generalization operators and three different levels of agreement between the clustering hierarchy obtained from the linkage distance and the hierarchy obtained by using generalization operators.Sociedad Argentina de Informática e Investigación Operativ
