Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been construct...

ions are made over the structural information (relations)

Fisher's Cobweb provided a well-defined framework for research on the unsupervised induction of probabilistic concept hierarchies. The system also sparked the development of many successors that extended this framework along various dimensions. In this paper, we summarize the assumptions that Cobweb embodies about the representation, organization, use, and formation of probabilistic concepts, along with experimental studies that examine its sources of power. After this, we consider three systems -- Arachne, Twilix, and Oxbow -- that incorporate significant extensions and present empirical evidence that these improve behavior. In closing, we discuss other paradigms for the unsupervised learning of probabilistic knowledge and their relation to the Cobweb framework. We thank our collaborators, including John Gennari, Kathleen McKusick, Kevin Thompson, and John Allen, for their contributions to the research described in this paper. Grant MDA 903-85-C0324 from the Army Research Insitute sup...

In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability...

Abstract. In this paper, a tool for Knowledge Discovery and Data Mining in environmental databases is presented. In the long term, the main goal of this research is to design and develop a tool, named GESCONDA, for intelligent data analysis and management of implicit knowledge from databases; it will provide support to Knowledge Discovery and Data Mining tasks that can guide the decision-making process, with special focus on environmental databases. The first stage of the project is to develop a prototype of the tool. Differing from the existing commercial systems, the more relevant aspects of this proposal are the possibility of interaction between the developed methods, the development of mixed techniques (combining tools from different fields, as AI or Statistics, that cooperate among them) to extract the knowledge contained in data, the existence of dynamical data analysis, and the existence of a recommender agent, which will suggest the best method to be used depending on the target domain and on the goals specified by users. The purpose of the paper is to present the architecture of the system as well as its functionality and to illustrate some of the possibilities of supporting knowledge discovery and data mining on environmental real domains. Finally, the use of GESCONDA in the context of environmental systems is presented, as well as results obtained in a concrete case study. 1