Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)

Flow Graph Network Based Non-redundant Correlative Educational Rules Discovered

Authors
Bo Liu, Changqin Huang, Xiuyu Lin
Corresponding Author
Bo Liu
Available Online December 2017.
DOI
https://doi.org/10.2991/anit-17.2018.5How to use a DOI?
Keywords
correlative rule, non-redundant rule, flow graph network
Abstract
A correlative rule expresses a relationship between two correlative events happening one after another. These rules are potentially useful for analyzing correlative data, ranging from purchase histories, web logs and program execution traces. In this work, we investigate and propose a syntactic characterization of a non-redundant set of correlative rules built upon past work on compact set of representative patterns. When using the set of mined rules as a composite filter, replacing a full set of rules with a non-redundant subset of the rules does not impact the accuracy of the filter. Lastly, we propose an algorithm to mine this compressed set of non-redundant rules. A performance study shows that the proposed algorithm significantly improves both the run-time and compactness of mined rules over mining a full set of sequential rules.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Bo Liu
AU  - Changqin Huang
AU  - Xiuyu Lin
PY  - 2017/12
DA  - 2017/12
TI  - Flow Graph Network Based Non-redundant Correlative Educational Rules Discovered
BT  - 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/anit-17.2018.5
DO  - https://doi.org/10.2991/anit-17.2018.5
ID  - Liu2017/12
ER  -