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.
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This work is supported by National Key Research and Development Program of China (Grant No. 2017YFB0701900), National Nature Science Foundation of China (Grant No. 61100053) and Key Laboratory of Machine Perception in Peking University (K-2019-09).
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Li, C., Dong, X., Liu, W. et al. SSRDVis: Interactive visualization for event sequences summarization and rare detection. J Vis 23, 171–184 (2020). https://doi.org/10.1007/s12650-019-00609-x
- Visual analytics
- Event sequences
- Sequential pattern mining
- Rare detection