Author(s)

R. Alves, O. Belo, F. Cavalcanti & P. Ferreira

Abstract

Collecting and mining clickstream data from e-commerce sites has become increasingly important for marketing, advertising, and traffic analysis activities. Organizations are promoting many initiatives concerning user’s navigation pattern discovery, in order to implement better sites, more functional and close to customers’ needs. Basically, the main idea is to provide more quality of attendance in their sites, and, consequently, get more profitability. However, clickstream processing is not a simple task. The sequences of clicks are very difficult to handle using conventional techniques, essentially due to their diversity and nature. They include a lot of aspects that reveal the multidimensional perspective of web data. OLAP technology provides today the means and techniques to represent, store and analyse such kinds of multidimensional data. However, it does not offer discovery driven analysis to support traversal pattern identification processes on web sites. Mining traversal pattern techniques can be applied in conjunction with OLAP as an integrated alternative for understanding those particular sequences of clicks. In this paper we present an integrated OLAP and mining approach specially conceived for exploring user navigation patterns based on clickstreams. We also describe the multidimensional structure provided for modelling click sequences and the OLAP operations and mining techniques that can be pushed over data cubes to bring up navigation patterns. 1 Introduction The concepts and techniques of data mining and knowledge discovery could be applied efficiently on web sites. Furthermore, this specific application of data mining called Web Mining, has taken much attention of researchers and

Keywords