Mining clickstream-based data cubes

Abstract

Clickstream analysis can reveal usage patterns on company's web sites giving highly improved understanding of customer behaviour. This can be used to improve customer satisfaction with the website and the company in general, yielding a great business advantage. Such information has to be extracted from very large collections of clickstreams in web sites. This is challenging data mining, both in terms of the magnitude of data involved, and the need to incrementally adapt the mined patterns and rules as new data is collected. In this paper, we present some guidelines for implementing on-line analytical mining engines which means an integration of on-line analytical processing and mining techniques for exploring multidimensional data cube structures. Additionally, we describe a data cube alternative for analyzing clickstreams. Besides, we discussed implementations that we consider efficient approaches on exploring multidimensional data cube structures, such as DBMiner, WebLobMiner, and OLAP-based Web Access Engine.