This article presents an algorithm that seeks every possible exception rule that violates a commonsense rule and satisfies several assumptions of simplicity. Exception rules, which represent systematic deviation from commonsense rules, are often found interesting. Discovery of pairs that consist of a commonsense rule and an exception rule, resulting from undirected search for unexpected exception rules, was successful in various domains. In the past, however, an exception rule represented a change of conclusion caused by adding an extra condition to the premise of a commonsense rule. That approach formalized only one type of exception and failed to represent other types. To provide a systematic treatment of exceptions, we categorize exception rules into 11 categories, and we propose a unified algorithm for discovering all of them. Preliminary results on 15 real-world datasets provide an empirical proof of effectiveness of our algorithm in discovering interesting knowledge. The empirical results also match our theoretical analysis of exceptions, showing that the 11 types can be partitioned in three classes according to the frequency with which they occur in data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 673–691, 2005.

We analyze relationships between different forms of knowledge that can be discovered in the same data matrix (relational table): contingency tables, equations, concept definitions, concept hierarchies, and rules. We argue that contingency tables are

We define the problem of empirical search for knowledge by interaction with a setup experiment, and we present a solution implemented in the FAHRENHEIT discovery system. FAHRENHEIT autonomously explores multi-dimensional empirical spaces of numerical

The BACON system, developed by Langley, Simon and Bradshaw, has shown the utility of a data driven discovery system. A new system, called FAHRENHEIT, has been built which extends BACON, making it more robust and allowing it perform a wider range of discovery activity. The new system extends BACON in several ways: 1) It determines the scope of a law by making simulated experiments, and by searching for regularities that describe the scope boundaries. 2) The world model in which the experiments are performed is more sophisticated. 3) The order in which the data are considered is placed under the control of FAHRENHEIT so that the system continues the discovery process even if no regularity is found for a particular variable.