Synthesizing heavy association rules from different real data sources-Reference-Cited by-同舟云学术

Synthesizing heavy association rules from different real data sources

Published:2008-01 Issue:1 Volume:29 Page:59-71
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Adhikari Animesh,Rao P.R.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference24 articles.

1. Parallel mining of association rules;Agrawal;IEEE Trans. Knowledge Data Eng.,1999

2. Agrawal, R., Srikant, R., 1994. Fast algorithms for mining association rules. In: Proceedings of the International Conference on Very Large Data Bases. pp. 487–499.

3. Agrawal, R., Imielinski, T., Swami, A., 1993. Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD Conference Management of Data. pp. 207–216.

4. Aronis, J., Kolluri, V., Provost, F., Buchanan, B., 1997. The WoRLD: Knowledge discovery from multiple distributed databases. In: Proceedings of the 10th International Florida AI Research Symposium. pp. 337–341.

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Improved Similarity-Based Clustering Algorithm for Multi-Database Mining;Entropy;2021-04-29

2. A Gradient-Based Clustering for Multi-Database Mining;IEEE Access;2021

3. A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data;Journal of Information & Knowledge Management;2019-03

4. F-NSP+: A fast negative sequential patterns mining method with self-adaptive data storage;Pattern Recognition;2018-12

5. Review on mining data from multiple data sources;Pattern Recognition Letters;2018-07