Title :
An improved algorithm of mining Strong Jumping Emerging Patterns based on sorted SJEP-Tree
Author :
Chen, Xiangtao ; Lu, Lijuan
Author_Institution :
Sch. of Comput. & Commun., Hunan Univ., Changsha, China
Abstract :
Jumping Emerging Patterns (JEPs) are a data mining model that is useful as a mean of discovering differences present amongst a collection of classified transaction datasets. However, current JEPs mining algorithms are usually time-consuming and pruning with minimum support may require several adjustments. In this paper, we investigate Strong Jumping Emerging Patterns (SJEPs), which are believed to be high quality patterns with the most differentiating power. We propose an improved tree-based method to effectively mine SJEPs of two data classes. Experimental results show that our algorithm is effective.
Keywords :
data mining; pattern classification; transaction processing; trees (mathematics); classified transaction dataset; data class; data mining; sorted SJEP-tree; strong jumping emerging pattern mining; tree-based method; ISO standards; Iris; Liver; data mining; jumping emerging patterns; strong jumping emerging patterns;
Conference_Titel :
Bio-Inspired Computing: Theories and Applications (BIC-TA), 2010 IEEE Fifth International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-6437-1
DOI :
10.1109/BICTA.2010.5645245