An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Introduction: Recent Developments in Parallel and Distributed Data Mining
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
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
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
| Hi-index | 0.00 |
The association mining is one of the primary sub-areas in the field of data mining. This technique had been used in numerous practical applications, including consumer market basket analysis, inferring patterns from web page access logs, network intrusion detection and pattern discovery in biological applications. Most of the traditional association-mining algorithms assume that whole dataset can be loaded in the main memory. Hence, problem arise when such algorithms is applied in large dataset. In this paper we present a new algorithm for association mining. Our algorithm is efficient when the size of dataset is huge that cannot be load in the main memory. The proposed algorithm also reduces the frequent itemsets search space, by eliminating non-frequent 1- itemsets after the first pass. Our performance evaluation shows algorithm out-performs Apriori algorithm in different datasets.