Adaptive Web Sites: Conceptual Cluster Mining

Abstract

Th e creation of a complex web site is a thorny problem in user interface design . In IJCAI '97, we challenged the AI community to address this problem by creating adaptive web sites. In response , we investigate the problem of index page synthesis - the automatic creation of pages that facilitate a visitor's navigation of a Web site. Previous work has employed statistical methods to generate candidate index pages that are of limited value because they do not correspond to concepts or topics that are intuitive to people. In this paper we formalize index page synthesis as a conceptual clustering problem and introduce a novel approach which we call conceptual cluster mining: we search for a small number of cohesive clusters that correspond to concepts in a given concept description language L . Next, we present SCML, an algorithm schema that combines a statistical clustering algorithm with a concept learning algorithm. The clustering algorithm is used to generate seed clusters, and the concept learning algorithm to describe these seed clusters using expressions in L. Finally, we offer preliminary experimental evidence that instantiations of SCML outperform existing algorithms (e.g., COBWEB) in this domain.