Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Algorithms
Introduction to parallel computing: design and analysis of algorithms
Introduction to parallel computing: design and analysis of algorithms
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Dynamic itemset counting and implication rules for market basket data
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
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Hash based parallel algorithms for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
A New and Versatile Method for Association Generation
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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
Effect of Data Skewness in Parallel Mining of Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
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
ScalParC: A New Scalable and Efficient Parallel Classification Algorithm for Mining Large Datasets
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
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The problem of mining hidden associations present in the large amounts of data has seen widespread applications in many practical domains such as customer-oriented planning and marketing, telecommunication network monitoring, and analyzing data from scientific experiments. The combinatorial complexity of the problem and phenomenal growth in the sizes of available datasets motivate the need for efficient and scalable parallel algorithms. The design of such algorithms is challenging. This chapter presents an evolutionary and comparative review of many existing representative serial and parallel algorithms for discovering two kinds of associations. The first part of the chapter is devoted to the non-sequential associations, which utilize the relationships between events that happen together. The second part is devoted to the more general and potentially more useful sequential associations, which utilize the temporal or sequential relationships between events. It is shown that many existing algorithms actually belong to a few categories which are decided by the broader design strategies. Overall the aim of the chapter is to provide a comprehensive account of the challenges and issues involved in effective parallel formulations of algorithms for discovering associations, and how various existing algorithms try to handle them.