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 clickstream 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 usingWeb data from an academic institution and an online Web store.
|Original language||English (US)|
|Title of host publication||WEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points - 3rd International Workshop, Revised Papers|
|Editors||Ron Kohavi, Brij M. Masand, Myra Spiliopoulou, Jaideep Srivastava|
|Number of pages||22|
|ISBN (Print)||3540439692, 9783540439691|
|State||Published - 2002|
|Event||3rd International Workshop on MiningWeb Log Data, WEBKDD, 2001 - San Francisco, United States|
Duration: Aug 26 2001 → Aug 26 2001
|Name||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|Other||3rd International Workshop on MiningWeb Log Data, WEBKDD, 2001|
|Period||8/26/01 → 8/26/01|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.