Work on first-order clustering has primarily been focused on the task of conceptual clustering, i.e., forming clusters with symbolic generalizations in the given representation language. By contrast, for propositional representations, experience has shown that simple algorithms based exclusively on distance measures can often outperform their concept-based counterparts. In this paper, we therefore build on recent advances in the area of #rst-order distance metrics and present RDBC, a bottom-up agglomerative clustering algorithm for #rst-order representations that relies on distance information only and features a novel parameter-free pruning measure for selecting the #nal clustering from the cluster tree. The algorithm can empirically be shown to produce good clusterings #on the mutagenesis domain# that, when used for subsequent prediction tasks, improve on previous clustering results and approach the accuracies of dedicated predictive learners.

this paper, we present an approach based on the use of two languages of description of classes for the automatic clustering of semistructured data. The first language of classes has a high power of abstraction and guides the construction of a lattice of classes covering the whole set of the data. The second language of classes, more expressive and more precise, is the basis for the refinement of a part of the lattice that the user wants to focus on. Our approach has been implemented and experimented on real data in the setting of the GAEL project which aims at building flexible electronic catalogs organized as a hierarchy of classes of products. Our experiments have been conducted on real data coming from the C/Net (http://www.cnet.com) electronic catalog of computer products

The field of knowledge discovery is concerned with the theory and processes involved in finding and representing patterns and regularities previously unknown. A new generation of knowledge discovery tools now deals with structured concepts: these capture associations between relations among the components of structured objects. This paper outlines a logic used to express structured concepts, and surveys a number of systems performing structured concept discovery. The paper concludes with a discussion of important future research directions for the field. Contents 1 Introduction and motivations 3 2 Structured concepts: theoretical foundations 3 2.1 Logical concepts : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.1.1 A simple example : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.2 Structured concepts : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.3 Subsumption of concepts : : : : : : : : : : : : : : : : : : : : : : : : : : ...

In many real-world applications, designers tend towards building classes of objects such as concepts, chunks and clusters according to some similarity criteria. In this paper, we first compare two approaches to clustering: the Galois lattice approach [14] and a similarity-based clustering approach [27]. Then, we sketch the possible ways each approach can benefit from the other in refining the process of building a hierarchy of classes out of a set of instances.