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definitiva in altra sede. Abstract. We propose a novel methodology for clustering XML documents on the basis of their structural similarities. The basic idea is to equip each cluster with an XML cluster representative, i.e. an XML document subsuming the most typical structural specifics of a set of XML documents. Clustering is essentially accomplished by(More)
This work presents an Application Domain model for Adaptive Hypermedia Systems and an architecture for its support. For the description of the high-level structure of the application domain we propose an object-oriented model based on the class diagrams of the Unified Modeling Language, extended with (i) a graph-based formalism for capturing navigational(More)
We propose a hierarchical, model-based co-clustering framework for handling high-dimensional datasets. The technique views the dataset as a joint probability distribution over row and column variables. Our approach starts by clustering tuples in a dataset, where each cluster is characterized by a different probability distribution. Subsequently, the(More)
In this work we propose DAEDALUS, a formal framework and system, specifically focussed on progressive combination of mining and querying operators. The core component of DAEDALUS is the MO-DMQL query language that extends SQL in two respects, namely a pattern definition operator and the capability to uniform manipulating both raw data and unveiled patterns.(More)
We propose an incremental technique for discovering duplicates in large databases of textual sequences, i.e. syntactically different tuples, that refer to the same real-world entity. The problem is approached from a clustering perspective: given a set of tuples, the objective is to partition them into groups of duplicate tuples. Each newly arrived tuple is(More)