Association Rules for Expressing Gradual Dependencies
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Adaptive Parallel Graph Mining for CMP Architectures
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Fast extraction of gradual association rules: a heuristic based method
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Mining Frequent Gradual Itemsets from Large Databases
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Mining tree-structured data on multicore systems
Proceedings of the VLDB Endowment
GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Fault prediction under the microscope: a closer look into HPC systems
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Gradual patterns highlight complex order correlations of the form “The more/less X, the more/less Y”. Only recently algorithms have appeared to mine efficiently gradual rules. However, due to the complexity of mining gradual rules, these algorithms cannot yet scale on huge real world datasets. In this paper, we propose to exploit parallelism in order to enhance the performances of the fastest existing one (GRITE). Through a detailed experimental study, we show that our parallel algorithm scales very well with the number of cores available.