Boolean Feature Discovery in Empirical Learning
Machine Learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine Learning
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fast discovery of association rules
Advances in knowledge discovery and data mining
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Efficient association rule mining among both frequent and infrequent items
Computers & Mathematics with Applications
A method for mining quantitative association rules
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Mining infrequent and interesting rules from transaction records
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
A neural-network model for learning domain rules based on its activation function characteristics
IEEE Transactions on Neural Networks
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
Extracting rules from trained neural networks
IEEE Transactions on Neural Networks
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To increase the efficiency of data mining is the emphasis in this field at present. Through the establishment of transaction-item association matrix, this paper changes the process of association rule mining to elementary matrix operation, which makes the process of data mining clear and simple. Compared with algorithms like Apriori, this method avoids the demerit of traversing the database repetitiously, and increases the efficiency of association rule mining obviously in the use of sparse storage technique for large-scale matrix. To incremental type of transaction matrix, it can also make the maintainment of association rule more convenient in the use of partitioning calculation technique of matrix. On the other and, aiming at the demerits in FP-growth algorithm, this paper proposes a FP-network model which compresses the data needed in association rule mining in a FP-network. Compared with the primary FP-tree model, the FP-network proposed is undirected, which enlarge the scale of transaction storage; furthermore, the FP-network is stored through the definition of transaction-item association matrix, it is convenient to make association rule mining on the basic of defining node capability. Experiment results show that the FP-network mining association rule algorithm proposed by this paper not only inherits the merits of FP-growth algorithm, but also maintains and updates data conveniently. It improves the efficiency of association rule mining significantly.