K-Optimal Pattern Discovery: An Efficient and Effective Approach to Exploratory Data Mining

Most data-mining techniques seek a single model that optimizes an objective function with respect to the data. In many real-world applications several models will equally optimize this function. However, they may not all equally satisfy a user's preferences, which will be affected by background knowledge and pragmatic considerations that are infeasible to quantify into an objective function. Thus, the program may make arbitrary and potentially suboptimal decisions. In contrast, methods for exploratory pattern discovery seek all models that satisfy userdefined criteria. This allows the user select between these models, rather than relinquishing control to the program. Association rule discovery [1] is the best known example of this approach. However, it is based on the minimum-support technique, by which patterns are only discovered that occur in the data more than a user-specified number of times. While this approach has proved very effective in many applications, it is subject t...

• Type: Conference
• Year: 2005
• Where: AUSAI
• Authors: Geoffrey I. Webb
• Keywords: Artificial Intelligence, AUSAI 2005, Exploratory Pattern Discovery, Objective Function, Pattern Discovery