Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ECML '95 Proceedings of the 8th European Conference on Machine Learning
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Real World Association Rule Mining
BNCOD 19 Proceedings of the 19th British National Conference on Databases: Advances in Databases
An introduction to variable and feature selection
The Journal of Machine Learning Research
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Bayesian networks and information retrieval: an introduction to the special issue
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Scrutinizing Frequent Pattern Discovery Performance
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
An implementation of the FP-growth algorithm
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Top 10 algorithms in data mining
Knowledge and Information Systems
A scalable algorithm for mining maximal frequent sequences using a sample
Knowledge and Information Systems
Estimating the number of frequent itemsets in a large database
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
pcApriori: scalable apriori for multiprocessor systems
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Scalable frequent itemset mining on many-core processors
Proceedings of the Ninth International Workshop on Data Management on New Hardware
| Hi-index | 0.00 |
Association rule mining is a particularly well studied field in data mining given its importance as a building block in many data analytics tasks. Many studies have focused on efficiency because the data to be mined is typically very large. However, while there are many approaches in literature, each approach claims to be the fastest for some given dataset. In other words, there is no clear winner. On the other hand, there is panoply of algorithms and implementations specifically designed for parallel computing. These solutions are typically implementations of sequential algorithms in a multi-processor configuration focusing on load balancing and data partitioning, each processor running the same implementation on it is own partition. The question we ask in this paper is whether there is a means to select the appropriate frequent itemset mining algorithm given a dataset and if each processor in a parallel implementation could select its own algorithm provided a given partition of the data.