Mining Association Rules from Stream Data Based on the Dynamic Support
Jia Luo, Shihe Chen, Fengping Pan, Yaqin Zhu, Le Wu, Yaqi Sun, Chunkai Zhang
Available Online January 2016.
- https://doi.org/10.2991/icaita-16.2016.9How to use a DOI?
- stream data; association rules; inter transaction; support threshold.
- The Stream data exists in the field of industrial production, life activities, business transactions, and other fields. It is closely related to people’s life, production and so on. This paper proposes inter-transaction association rules mining method based on dynamic support threshold. Inter-transaction association rules refer to the association rules between different time periods. This paper firstly uses the sliding window to limit stream data, then do preprocessing on stream data. In the process of pretreatment using linearization method fitting to raw data and it reduce the amount of data at the same time, and finally at the end of the preprocessing, generating large transaction grouping method of inter transaction association rules is proposed in this paper. This paper uses conceptual data attenuation, thereby reducing the influence of old data to the mining result. Due to artificial setting minimum support threshold may bring many problems, so this paper presents a method for searching minimum support threshold.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jia Luo AU - Shihe Chen AU - Fengping Pan AU - Yaqin Zhu AU - Le Wu AU - Yaqi Sun AU - Chunkai Zhang PY - 2016/01 DA - 2016/01 TI - Mining Association Rules from Stream Data Based on the Dynamic Support BT - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications PB - Atlantis Press SP - 34 EP - 37 SN - 1951-6851 UR - https://doi.org/10.2991/icaita-16.2016.9 DO - https://doi.org/10.2991/icaita-16.2016.9 ID - Luo2016/01 ER -