Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A study of evolutionary multiagent models based on symbiosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, we introduce a method of Generalized Association Rule Mining using Genetic Network Programming (GNP) with time series processing mechanism in order to find time related sequential rules efficiently. GNP represents solutions as directed graph structures, thus has compact structure and implicit memory function. The inherent features of GNP make it possible for GNP to work well especially in dynamic environments. GNP has been applied to generate time related candidate association rules as a tool using the database consisting of a large number of time related attributes. The aim of this algorithm is to better handle association rule extraction from the databases in a variety of time-related applications, especially in the traffic volume prediction problems. The generalized algorithm which can find the important time related association rules is described and experimental results are presented considering a traffic prediction problem.