Gradual inference rules in approximate reasoning
Information Sciences: an International Journal
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Implication-Based Fuzzy Association Rules
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Association Rules for Expressing Gradual Dependencies
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On Data Summaries Based on Gradual Rules
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Gradual elements in a fuzzy set
Soft Computing - A Fusion of Foundations, Methodologies and Applications
GRAANK: Exploiting Rank Correlations for Extracting Gradual Itemsets
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Mining closed gradual patterns
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
PGP-mc: towards a multicore parallel approach for mining gradual patterns
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
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
Para Miner: a generic pattern mining algorithm for multi-core architectures
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Mining gradual rules plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual rules highlight complex order correlations of the form "The more/less X, then the more/less Y ". Such rules have been studied since the early 70's, mostly in the fuzzy logic domain, where the main efforts have been focused on how to model and use such rules. However, mining gradual rules remains challenging because of the exponential combination space to explore. In this paper, we tackle the particular problem of handling huge volumes by proposing scalable methods. First, we formally define gradual association rules and we propose an original lattice-based approach. The GRITE algorithm is proposed for extracting gradual itemsets in an efficient manner. An experimental study on large-scale synthetic and real datasets is performed, showing the efficiency and interest of our approach.