We propose an efficient algorithm for mining frequent approximate sequential patterns under the Hamming distance model. Our algorithm gains its efficiency by adopting a "brea...
In many real world applications, systematic analysis of rare events, such as credit card frauds and adverse drug reactions, is very important. Their low occurrence rate in large da...
Jie Chen, Hongxing He, Graham J. Williams, Huidong...
Biosequences typically have a small alphabet, a long length, and patterns containing gaps (i.e., “don’t care”) of arbitrary size. Mining frequent patterns in such sequences ...
Previous work on text mining has almost exclusively focused on a single stream. However, we often have available multiple text streams indexed by the same set of time points (call...
Xuanhui Wang, ChengXiang Zhai, Xiao Hu, Richard Sp...
The web log data embed much of web users' browsing behavior. From the web logs, one can discover patterns that predict the users' future requests based on their current b...