Using Genetic Algorithm for Data Mining Optimization in an Image Database
Li Gao, Shangping Dai, Shijue Zheng, Guanxiang Yan · 2007
Data Mining is rapidly evolving areas of research that are at the intersection of several disciplines, including statistics, databases, pattern recognition, and high- performance and parallel computing. In this paper, we propose a novel mining algorithm, called ARMAGA (association rules mining algorithm based on a novel genetic algorithm), to mine the association rules from an image database, where every image is represented by the ARMAGA representation. We first take advantage of the genetic algorithm designed specifically for discovering association rules. Second we propose the algorithm compared to the algorithm in (Chen and Wei, 2002), and the ARMAGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.