A Parallel Distributive Join Algorithm for Cube-Connected Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Parallel sequence mining on shared-memory machines
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
MPI: The Complete Reference
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Parallel Tree Projection Algorithm for Sequence Mining
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A new two-phase sampling based algorithm for discovering association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of sampling for data mining of association rules
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
A scalable algorithm for mining maximal frequent sequences using a sample
Knowledge and Information Systems
BIDE-Based parallel mining of frequent closed sequences with mapreduce
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
| Hi-index | 0.00 |
In this paper, we propose a new parallel algorithm, named PMSPX, which mines maximal frequent sequences by using multiple samples to exclude infrequent candidates effectively. A frequent sequence is maximal if none of its supersequences is frequent. Unlike the traditional single-sample methods developed for mining frequent itemsets, PMSPX uses multiple samples. Thus, it can avoid or alleviate some problems inherent in the single-sample methods. We theoretically analyzed how to increase the minimum support level to prevent misestimating infrequent candidates as frequent in the mining of samples. PMSPX is a parallel version of our sequential MSPX algorithm, and it is developed on a cluster of workstations. In PMSPX, each processing node uses MSPX to find a candidate set of local maximal frequent sequences first, independently from other processing nodes. Then, a top-down search is performed, starting with all the candidates, in a synchronous manner to identify real maximal frequent sequences. This asynchronous local mining followed by synchronous global mining approach minimizes the synchronization and communication among the processing nodes. Three database partitioning methods are proposed to distribute the database across the processing nodes, so that their workloads are balanced and the data skewness of the whole database is preserved in the data partition of each node. A comprehensive analysis was performed on PMSPX and existing parallel sequence mining algorithms, and extensive experiments were conducted on PMSPX. PMSPX demonstrates very good speedup and scaleup properties. It also requires less communication and synchronization than other parallel algorithms.