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Genetic Network Programming (GNP) is one of the evolutionary algorithms. It adopts a directed graph structure to represent a solution to a given problem. Agents judge situations and execute actions sequentially following the node transitions in the graph. On one hand, GNP possesses an advantage of node reusability, which makes it possible to realize a compact graph structure that represents a solution...
In this paper, backward time related association rule mining using genetic network programming (GNP) with database rearrangement is introduced in order to find time related sequential association from time related databases effectively and efficiently. GNP is a kind of human brain like evolutionary model which represents solutions as directed graph structures. The concept of database rearrangement...
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...
We propose a method of association rule mining using genetic network programming (GNP) with time series processing mechanism and attribute accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. We suppose that, the database consists of a large number of attributes based on time series. In order to deal with databases which have a large...
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