Mining Adaptive Ratio Rules from Distributed Data Sources

Different from traditional association-rule mining, a new paradigm called Ratio Rule (RR) was proposed recently. Ratio rules are aimed at capturing the quantitative association knowledge, We extend this framework to mining ratio rules from distributed and dynamic data sources. This is a novel and challenging problem. The traditional techniques used for ratio rule mining is an eigen-system analysis which can often fall victim to noise. This has limited the application of ratio rule mining greatly. The distributed data sources impose additional constraints for the mining procedure to be robust in the presence of noise, because it is difficult to clean all the data sources in real time in real-world tasks. In addition, the traditional batch methods for ratio rule mining cannot cope with dynamic data. In this paper, we propose an integrated method to mining ratio rules from distributed and changing data sources, by first mining the ratio rules from each data source separately through a nov...

• Type: Journal
• Year: 2006
• Where: DATAMINE
• Authors: Jun Yan, Ning Liu, Qiang Yang, Benyu Zhang, QianSheng Cheng, Zheng Chen
• Keywords: Data Source, DATAMINE 2006, Mining, Ratio Rule