Mining Temporal Association Rules with Temporal Soft Sets
Metadatos
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Hindawi
Fecha
2021-11-29Referencia bibliográfica
Xiaoyan Liu... [et al.]. "Mining Temporal Association Rules with Temporal Soft Sets", Journal of Mathematics, vol. 2021, Article ID 7303720, 17 pages, 2021. [https://doi.org/10.1155/2021/7303720]
Patrocinador
National Natural Science Foundation of China (NSFC) 11301415; Shaanxi Provincial Key Research and Development Program 2021SF-480; Natural Science Basic Research Plan in Shaanxi Province of China 2018JM1054Resumen
Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data.
Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent
in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We
define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define
temporal soft sets and their Q-clip soft sets to establish a novel framework for mining temporal association rules. A number of
useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong
temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we
develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association
rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover,
comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known
approaches in the literature.