Title :
A new closed frequent items mining from tree data
Author :
Qinsheng Du ; Xiongfei Li ; Wei Li
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Abstract :
Mining the frequent sub-patterns from tree dataset is a hot research topic, and a lot of research work has done on the frequent item field. Mining frequent item sets is a key step in many data mining problems, such as association rule mining, sequential pattern mining, classification, and so on. This paper proposed a data model named TTDOM to record the history changing process. TTDOM utilize two additional time tags ("ins" and "del") describe the changes of each node of tree data. Then, an algorithm is proposed to discover all the sub-trees in the TTDOM dataset. Finally, we use bitmap method to close the mining result. The experiment result shows that our approach is efficient.
Keywords :
data mining; pattern classification; tree data structures; TTDOM; association rule mining; bitmap method; classification; closed frequent items mining; data mining problems; sequential pattern mining; subtrees; time tags; tree dataset; Data mining; Data models; Educational institutions; Finite element analysis; Periodic structures; XML; TTDOM; data mining; frequent sub-pattern; tree data;
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2013 3rd International Conference on
Conference_Location :
Dalian
DOI :
10.1109/ICCSNT.2013.6967139