期刊
JOURNAL OF VISUALIZATION
卷 23, 期 1, 页码 171-184出版社
SPRINGER
DOI: 10.1007/s12650-019-00609-x
关键词
Visual analytics; Event sequences; Sequential pattern mining; Rare detection
This paper presents SSRDVis, a visual approach to effectively summarize event sequences and interactively detect rare behaviors. SSRDVis is mainly composed of three components: (1) a sequence embedding module for learning effective feature vectors of sequences, (2) a sequence grouping and summarization module to find representative clusters and patterns in the dataset, (3) a rare detection module to discover and explain the rare cases. The sequences are embedded into vector space via mixed-ngram2vec, which is adapted from word2vec. Then, unsupervised learning models could be applied to group similar sequences and detect anomalies in the vector space. Furthermore, sequential pattern graphs are built to provide a compact and semantic summarization of sequences. These components work together to present both overall sequential patterns and abnormal behaviors in one visual interface. We have demonstrated the feasibility of our approach by applying it to analyze Web clickstreams. Experimental results have shown that our approach could help identify noticeable patterns from a large number of event sequences, especially for rare behaviors. Graphic abstract
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据