Title :
Mining up-to-date knowledge from log data
Author :
Hong, Tzung-Pei ; Wu, Yi-Ying ; Wang, Shyue-Liang
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung
Abstract :
In this paper, a new concept of up-to-date patterns is proposed, which is a hybrid of the association rules and temporal mining. An up-to-date pattern is composed of an item set and its up-to-date lifetime, in which the user-defined minimum support threshold must be satisfied. The proposed approach can mine more useful large itemsets than the conventional ones which discover large itemsets valid only for the entire database. Experimental results also show the performance of the proposed approach.
Keywords :
data mining; association rule; temporal mining; up-to-date knowledge pattern; user-defined minimum support threshold; Association rules; Clustering algorithms; Computer science; Data analysis; Data engineering; Data mining; Information management; Itemsets; Knowledge engineering; Transaction databases;
Conference_Titel :
Granular Computing, 2008. GrC 2008. IEEE International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-2512-9
Electronic_ISBN :
978-1-4244-2513-6
DOI :
10.1109/GRC.2008.4664772
