Title :
A Novel Genetic Algorithm Based on Image Databases for Mining Association Rules
Author :
Dai, Shangping ; Gao, Li ; Zhu, Qiang ; Zhu, Changwu
Author_Institution :
Hua Zhong Normal Univ., Wuhan
Abstract :
Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Mining encompasses various algorithms such as clustering, classification, and association rule mining. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(association rules mining in novel genetic algorithm), Compared to the algorithm in [Agrawal R, et al., Mining association rules between sets of items in large databases, 1993], the ARMNGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.
Keywords :
data mining; genetic algorithms; visual databases; data mining technology; genetic algorithm; image databases; mining association rules; Algorithm design and analysis; Association rules; Biological cells; Clustering algorithms; Computer science; Data mining; Frequency measurement; Genetic algorithms; Genetic mutations; Image databases;
Conference_Titel :
Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on
Conference_Location :
Melbourne, Qld.
Print_ISBN :
0-7695-2841-4
DOI :
10.1109/ICIS.2007.40