Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Loop scheduling for heterogeneity
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors
Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors
A localized algorithm for parallel association mining
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors
Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures
High performance data mining (tutorial PM-3)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Communication-efficient distributed mining of association rules
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Pthreads for dynamic and irregular parallelism
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Effect of Data Distribution in Parallel Mining of Associations
Data Mining and Knowledge Discovery
An Adaptive Algorithm for Mining Association Rules on Shared-Memory Parallel Machines
Distributed and Parallel Databases
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Design and Evaluation of a High-Level Interface for Data Mining
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
An efficient association mining implementation on clusters of SMP
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Adaptive Algorithms for Cache-Efficient Trie Search
ALENEX '99 Selected papers from the International Workshop on Algorithm Engineering and Experimentation
Parallel and Distributed Data Mining: An Introduction
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Parallel Sequence Mining on Shared-Memory Machines
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Communication-Efficient Distributed Mining of Association Rules
Data Mining and Knowledge Discovery
A Super-Programming Approach for Mining Association Rules in Parallel on PC Clusters
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Knowledge and Data Engineering
A high-performance distributed algorithm for mining association rules
Knowledge and Information Systems
A methodology for detailed performance modeling of reduction computations on SMP machines
Performance Evaluation - Performance modelling and evaluation of high-performance parallel and distributed systems
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Domain and data partitioning for parallel mining of frequent closed itemsets
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Finding association rules of cis-regulatory elements involved in alternative splicing
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
A Fast Parallel Association Rules Mining Algorithm Based on FP-Forest
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Mining of cis-Regulatory Motifs Associated with Tissue-Specific Alternative Splicing
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
A load-balanced distributed parallel mining algorithm
Expert Systems with Applications: An International Journal
Performance characterization of data mining benchmarks
Proceedings of the 2010 Workshop on Interaction between Compilers and Computer Architecture
Compiler and middleware support for scalable data mining
LCPC'01 Proceedings of the 14th international conference on Languages and compilers for parallel computing
Performance study of distributed Apriori-like frequent itemsets mining
Knowledge and Information Systems
RMS-TM: a comprehensive benchmark suite for transactional memory systems
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Compiler and runtime support for shared memory parallelization of data mining algorithms
LCPC'02 Proceedings of the 15th international conference on Languages and Compilers for Parallel Computing
Parallel approaches to machine learning-A comprehensive survey
Journal of Parallel and Distributed Computing
pcApriori: scalable apriori for multiprocessor systems
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
The Journal of Supercomputing
Accelerating frequent itemset mining on graphics processing units
The Journal of Supercomputing
Hi-index | 0.00 |
Data mining is an emerging research area, whose goal is to extract significant patterns or interesting rules from large databases. High-level inference from large volumes of routine business data can provide valuable information to businesses, such as customer buying patterns, shelving criterion in supermarkets and stock trends. Many algorithms have been proposed for data mining of association rules. However, research so far has mainly focused on sequential algorithms. In this paper we present parallel algorithms for data mining of association rules, and study the degree of parallelism, synchronization, and data locality issues on the SGI Power Challenge shared-memory multi-processor. We further present a set of optimizations for the sequential and parallel algorithms.Experiments show that a significant improvement of performance is achieved using our proposed optimizations. We also achieved good speed-up for the parallel algorithm, but we observe a need for parallel I/O techniques for further performance gains.