Abstract
One of the drawbacks of frequent episode mining is that overwhelmingly many of the discovered patterns are redundant. Free-rider episode, as a typical example, consists of a real pattern doped with some additional noise events. Because of the possible high support of the inside noise events, such free-rider episodes may have abnormally high support that they cannot be filtered by frequency based framework. An effective technique for filtering free-rider episodes is using a partition model to divide an episode into two consecutive subepisodes and comparing the observed support of such episode with its expected support under the assumption that these two subepisodes occur independently. In this paper, we take more complex subepisodes into consideration and develop a novel partition model named EDP for free-rider episode filtering from a given set of episodes. It combines (1) a dual partition strategy which divides an episode to an underlying real pattern and potential noises; (2) a novel definition of the expected support of a free-rider episode based on the proposed partition strategy. We can deem the episode interesting if the observed support is substantially higher than the expected support estimated by our model. The experiments on synthetic and real-world datasets demonstrate EDP can effectively filter free-rider episodes compared with existing state-of-the-arts.
Z. Huang—This work was done when Zhen was visiting Institute of Computing Technology, CAS.
The original version of this chapter was revised: For detailed information please see the erratum to this chapter available at https://doi.org/10.1007/978-3-319-91452-7_61
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Change history
01 July 2018
The original version of this chapter titled “Free-Rider Episode Screening via Dual Partition Model” contained the following three mistakes:
- 1.
In table 1, row 2, column 3, the average occurrence per event on STK dataset was “1.037”. It should be “1,037”.
- 2.
The last model name in the legend of Figure 3 was “EIP”. It should be “EDP”.
- 3.
In the experiment part the stock symbols and their companies were confused.
In the updated version these mistakes were corrected.
In the originally published version of chapters titled “BASSI: Balance and Status Combined Signed Network Embedding” and “Sample Location Selection for Efficient Distance-Aware Influence Maximization in Geo-Social Networks” the funding information in the acknowledgement section was incomplete. This has now been corrected.
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Notes
- 1.
\(p_{ ind }(e)\) can be calculated by \(\frac{\mathrm {sp}(e)}{\mathrm {len}(\varvec{S})}\), where \(\mathrm {sp}(e)\) is the support of event e in \(\varvec{S}\).
- 2.
Here we do not take any frequent event or the episode consisting of single event into account.
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Acknowledgement
This research is supported by the National Natural Science Foundation of China (No. 61602438, 91546122, 61573335), National key R&D program of China (No. 2017YFB1002104), Guangdong provincial science and technology plan projects (No. 2015B010109005).
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Ao, X., Liu, Y., Huang, Z., Zuo, L., He, Q. (2018). Free-Rider Episode Screening via Dual Partition Model. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_43
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