Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (10): 71-76.doi: 10.11896/jsjkx.190100223

• Big Data & Data Science • Previous Articles     Next Articles

Association Rule Mining Algorithm Based on Timestamp and Vertical Format

WANG Bin, MA Jun-jie, FANG Xin-xiu, WEI Tian-you   

  1. (School of Information and Control Engineering,Qingdao University of Technology,Qingdao,Shandong 266520,China)
  • Received:2019-01-26 Revised:2019-05-05 Online:2019-10-15 Published:2019-10-21

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Abstract: The SLMCM algorithm (Specific Later-marketed Consequent Mining) is mainly used to solve the problem of later item,but it is inefficient and difficult to adapt to big data mining.For this problem,this paper proposed the improved algorithms E-SLMCM and DE-SLMCM .E-SLMCM algorithm is based on vertical structure,so it only traversal the database twice.Furthermore,the timestamp of each item can be directly calculated when the format is converted to vertical,and each transaction does not need to be sorted by the timestamp of the item.In addition,a new method for finding the itemset timestamp was proposed,which dose not need to traverse the database to find the timestamp of itemset.In order to adapt to dense database,DE-SLMCM algorithm was proposed based on E-SLMCM algorithm and diffset,which improves the execution efficiency on dense database.In the listed four simulation experiments based on common data sets,the time efficiency of E-SLMCM and DE-SLMCM running on sparse and dense data sets is 10-1000 times higher than that of SLMCM.

Key words: Association rules, Diffset, Later item, Timestamp

CLC Number: 

  • TP391
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