article · 2004
Parallel mining association rules with bit string array in large database
Xiangping Meng, Jin Qian, Xin Qi
Cited by 0Abstract and details only
Abstract
Association rule mining is one of the important data mining tasks. However, the previously proposed methods still encounter some problems, such as complex data structure, candidate set generation, and so on. To improve efficiency, association rules can be mined in parallel. In this paper, we use a simpler data structure called bit string array and propose a new approach to apply parallel projection and compress technique in parallel mining association rules. It conducts various operations on bit string array according to the frequency of frequent items. For frequent item with less frequency, we conduct set operation on them; for frequent item with more frequency, we adopt compress technique and conduct bit AND operator on them. Moreover, it will reduce the communication cost and also response time. This method can be scaled up to very large databases by parallel projection and compress technique.
Cite this paper
Meng, X., Qian, J., & Qi, X. (2004). Parallel mining association rules with bit string array in large database. https://doi.org/10.1109/icmlc.2003.1264467
Meng, Xiangping, et al. “Parallel mining association rules with bit string array in large database.” 2004, pp. 183–87, https://doi.org/10.1109/icmlc.2003.1264467.
Meng, Xiangping, Jin Qian, and Xin Qi. 2004. “Parallel mining association rules with bit string array in large database.” https://doi.org/10.1109/icmlc.2003.1264467.
Meng, X., Qian, J. and Qi, X. (2004) ‘Parallel mining association rules with bit string array in large database’. Available at: https://doi.org/10.1109/icmlc.2003.1264467.
@article{meng2004parallel,
title = {{Parallel mining association rules with bit string array in large database}},
author = {Meng, Xiangping and Qian, Jin and Qi, Xin},
pages = {183--187},
year = {2004},
doi = {10.1109/icmlc.2003.1264467}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.