An Efficient Approach for Mining Periodic Sequential Access Patterns

Abstract

AbstractWeb usage mining discovers interesting and frequent user access patterns from web logs. Most of the previous works have focused on mining common sequential access patterns of web access events that occurred within the entire duration of all web access transactions. However, many useful sequential access patterns occur frequently only during a particular periodic time interval due to user browsing behaviors and habits. It is therefore important to mine periodic sequential access patterns with periodic time constraints. In this paper, we propose an efficient approach, known as TCS-mine (Temporal Conditional Sequence mining algorithm), for mining periodic sequential access patterns based on calendar-based periodic time constraints. The calendar-based periodic time constraints are used for describing real-life periodic time concepts such as the morning of every weekend. The mined periodic sequential access patterns can be used for temporal-based personalized web recommendations. The performance of the proposed TCS-mine algorithm is evaluated and compared with a modified version of WAP-mine for mining periodic sequential access patterns.KeywordsAccess PatternSingle SequenceSupport ThresholdMining Sequential PatternEvent QueueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.