Title :
Comparative of data base evolution in rule association algorithms in incremental and conventional way
Author :
Farias, Euclides Peres, Jr. ; Nievola, Jùlio Cesar
Abstract :
Many results in the literature indicate that the incremental approach to association mining leads to gain regarding the time needed to obtain the rules, but there is no evaluation about their quality, compared to non-incremental algorithms. This paper presents the comparison of usage of two typical algorithms representing each approach: APriori and ZigZag. Execution time clearly shows the advantage of incremental approaches, but when someone needs accurate results concerning the association rules obtained, the matter should be taken with more caution, because the rules obtained are not necessarily in a relation one-to-one, according to the results obtained.
Keywords :
data mining; database management systems; learning (artificial intelligence); APriori strategy; ZigZag strategy; association rule mining; database evolution; incremental algorithm; Association rules; Data mining; Graphical models; Itemsets; Transaction databases;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634241