Every time a user links up to a web site, the server keeps track of all the transactions accomplished in a log file. What is captured is the "click flow" (clickstream) of the mouse and the keys used by the user during the navigation inside the site. Usually every click of the mouse corresponds to the viewing of a web page. The objective of this chapter is to show how web clickstream data can be used to understand the most likely paths of navigation in a web site, with the aim of predicting, possibly on-line, which pages will be seen, having seen a specific path of other pages before. Such analysis can be very useful to understand, for instance, what is the probability of seeing a page of interest (such as the buying page in an e-commerce site) coming from another page. Or what is the probability of entering (or exiting) the web site from any particular page. From a methodological viewpoint, we present two main research contributions. On one hand we show how to improve the efficiency of the Apriori algorithm; on the other hand we show how Markov chain models can be usefully developed and implemented for web usage mining. In both cases we compare the results obtained with classical association rules algorithms and models.