Topic Modeling for Sequences of Temporal Activities
Shen, Zhi-Yong; Luo, Ping; Xiong, Yuhong; Sun, Jun; Shen, Yi-Dong
Keyword(s): topic modeling, LDA, sequence, temporal activities
Abstract: Temporally-ordered activity sequences are popular in many real-world domains. This paper presents an LDA- style topic model for sequences of temporal activities that captures three features of such sequences: 1) the counts of unique activities, 2) the Markov transition dependence and 3) the absolute or relative timestamp on each activity. In modeling the first two features we propose the concept of global transition probability and distinguish it with local transition probability used in previous work. In modeling the third feature, we employ a continuous time distribution to depict the time range of latent topics. The combination of the global transition probability and the temporal information helps to refine the mixture distribution over topics for temporal sequence analysis. We present results on the data of distributed denial-of-service attack and system call traces, qualitatively and quantitatively showing improved topics, better next activity prediction and sequence clustering.
Additional Publication Information: Published in the Ninth IEEE International Conference on Data Mining, Miami, Florida, USA, December 6-9, 2009
External Posting Date: October 21, 2010 [Fulltext]. Approved for External Publication
Internal Posting Date: October 21, 2010 [Fulltext]