hill climbing Abstract. Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains. 1.

Current large scale information sources are designed to support general queries and lack the ability to support scenario specific information navigation, gathering, and presentation. As a result, users are often unable to obtain desired specific information within a well defined subject area. Today's information systems do not provide efficient content navigation, incremental appropriate matching, or content correlation. We are developing the following innovative technologies to remedy these problems: (1) Scenario-based proxies, enabling the gathering and filtering of information customized for users within a pre-defined domain; (2) Context-sensitive navigation and matching, providing approximate matching and similarity links when an exact match to a user's request is unavailable; (3) Content correlation of documents, creating semantic links between documents and information sources; and (4) User models for customization of retrieved information and result presentation. A digital medical library is currently being constructed using these technologies to provide customized information for the user. The technologies are general in nature and can provide custom and scenario-specific information in many other domains (e.g. crisis management).

this paper, we present a joint concept formation system, SGNN, that extends the previous work of concept formation [Fisher, 1987; McKusick and Langley, 1991]. SGNN is able to generate either disjoint concept trees or acyclic directed concept graphs, according to the characteristics inherited in domain data. Furthermore, with certain controls applied to the number of winners at each concept layer, SGNN can also be used to only construct disjoint concept trees regardless of the regularity inherited in data. It is demonstrated 15