Author(s)

F. Cavalcanti & O. Belo

Abstract

The increasing necessity of organizational data exploration and analysis, seeking new knowledge that may be implicit in operational systems, has made the study of data mining techniques gain a huge impulse. This impulse can be clearly noticed in the e-commerce domain, where the analysis of a client’s past behaviours is extremely valuable and may, eventually, bring up important working instruments for determining their future behaviour. Typical databases are continuously changing, which can invalidate some patterns or introduce new ones. Thus, conventional data mining techniques were proved to be inefficient, as they needed to re-execute to update the mining results with the ones derived from the database changes. To solve this problem and ensure a better performance in data mining processes, incremental mining techniques have emerged. In this paper, we analyze some existing incremental mining strategies and models, giving particular emphasis to their application on Web sites, in order to develop models to discover Web users behaviour patterns and automatically generate some recommendations to restructure sites in useful time. Keywords: data mining, clickstreams, algorithms and strategies for incremental data mining and Web site restructuring. 1 Introduction Daily, in extremely dynamic market sectors, companies challenge a situation where information quality and knowledge over them play an important and vital role. In order to publish their goods and products to the customers, so they can be largely consumed, companies need to understand how customers behave in the market. The customer behaviour analysis can provide valuable information for a

Keywords

data mining, clickstreams, algorithms and strategies for incremental data mining and Web site restructuring.