Web associations are valuable patterns because they provide useful insights into the browsing behavior of Web users. However, there are two major drawbacks of using current techniques for mining Web association patterns, namely, their inability to detect interesting negative associations in data and their failure to account for the impact of site structure on the support of a pattern. To address these issues, a new data mining technique called indirect association is applied to the Web click-stream data. The idea here is to find pairs of pages that are negatively associated with each other, but are positively associated with another set of pages called the mediator. These pairs of pages are said to be indirectly associated via their common mediator. Indirect associations are interesting patterns because they represent the diverse interests of Web users who share a similar traversal path. These patterns are not easily found using existing data mining techniques unless the groups of users are known a priori. The effectiveness of indirect association is demonstrated using Web data from an academic institution and an online Web store.