Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel sequence mining on shared-memory machines
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Communication-efficient distributed mining of association rules
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Parallel Association Rule Mining without Candidacy Generation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Mining Sequential Alarm Patterns in a Telecommunication Database
DBTel '01 Proceedings of the VLDB 2001 International Workshop on Databases in Telecommunications II
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A High-Performance Distributed Algorithm for Mining Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Frequent Pattern Mining on Message Passing Multiprocessor Systems
Distributed and Parallel Databases
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Parallel tree-projection-based sequence mining algorithms
Parallel Computing
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Scalable sequential pattern mining for biological sequences
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Proceedings of the 2006 workshop on Memory system performance and correctness
Toward terabyte pattern mining: an architecture-conscious solution
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Data & Knowledge Engineering
Frequent itemset mining on graphics processors
Proceedings of the Fifth International Workshop on Data Management on New Hardware
Apriori-based frequent itemset mining algorithms on MapReduce
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Proceedings of the WICSA/ECSA 2012 Companion Volume
PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient mining of frequent itemsets in social network data based on MapReduce framework
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data mining problems with broad applications. Perhaps the most efficient way to solve these problems sequentially is to apply a pattern-growth algorithm, which is a divide-and-conquer algorithm [9, 10]. In this paper, we present a framework for parallel mining frequent itemsets and sequential patterns based on the divide-and-conquer strategy of pattern growth. Then, we discuss the load balancing problem and introduce a sampling technique, called selective sampling, to address this problem. We implemented parallel versions of both frequent itemsets and sequential pattern mining algorithms following our framework. The experimental results show that our parallel algorithms usually achieve excellent speedups.