Title :
An improved prefixspan algorithm research for sequential pattern mining
Author :
Liu Pei-yu ; Gong Wei ; Jia Xian
Author_Institution :
Sch. of Inf. Sci. & Eng., Shandong Normal Univ., Ji´nan, China
Abstract :
PrefixSpan, the classic sequential patterns mining algorithm, has the problem of large expenses in constructing projected databases. A Sequential Patterns Mining based on Improved PrefixSpan (SPMIP) algorithm is proposed on the basic of the defects above. This algorithm can reduce the scale of projected databases and the time of scanning projected databases through adding the pruning step and reducing the scanning of certain specific sequential patterns production. In this way, algorithm efficiency can be raised, and the sequential patterns needed are obtained. The experiment results show that the SPMIP algorithm is more efficient than the PrefixSpan algorithm while the sequential patterns obtained are not affected.
Keywords :
data mining; database management systems; pattern clustering; sequences; SPMIP algorithm; improved PrefixSpan algorithm; projected database; pruning step; sequential pattern mining; Algorithm design and analysis; Concrete; Data mining; Databases; Heuristic algorithms; Production; Software algorithms; PrefixSpan; Project database; Pruning; Scanning; Squential patterns;
Conference_Titel :
IT in Medicine and Education (ITME), 2011 International Symposium on
Conference_Location :
Cuangzhou
Print_ISBN :
978-1-61284-701-6
DOI :
10.1109/ITiME.2011.6130794