| 题名: | topic modeling for sequences of temporal activities |
| 作者: | Shen Zhi-Yong
; Luo Ping
; Xiong Yuhong
; Sun Jun
; Shen Yi-Dong
|
| 会议文集: | HP Laboratories Technical Report
|
| 会议名称: | topic modeling for sequences of temporal activities
|
| 会议日期: | 2010
|
| 出版日期: | 2010
|
| 会议地点: | 北京
|
| 关键词: | Computer crime
|
| 出版地: | United States
|
| 收录类别: | EI
|
| 部门归属: | (1) Institute of Software, CAS, China; (2) Graduate University of Chinese Academy of Sciences, China; (3) Hewlett Packard Labs China, China
|
| 英文摘要: | 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. ©Copyright The Ninth IEEE International Conference on Data Mining, 2009. |
| 内容类型: | 会议论文
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| URI标识: | http://ir.iscas.ac.cn/handle/311060/8944
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| Appears in Collections: | 计算机科学国家重点实验室 _会议论文
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| HPL-2010-160.pdf（321KB） | -- | -- | 限制开放 | -- | 联系获取全文 |
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| Recommended Citation: | |
Shen Zhi-Yong,Luo Ping,Xiong Yuhong,et al. topic modeling for sequences of temporal activities[C]. 见:topic modeling for sequences of temporal activities. 北京. 2010.
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