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一种快速挖掘top-k高效用模式的算法 被引量:5

Algorithm for fast discovery of top-k high utility patterns
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摘要 高效用模式挖掘是数据挖掘领域的一个基础研究方向,其中关于top-k高效用模式的挖掘算法也越来越多,k指的是用户需要挖掘的高效用模式的个数。它们可以归纳为二阶段top-k算法和一阶段top-k算法两类,两者的主要区别是,前者在挖掘的过程中会产生大量的候选模式,这是影响算法性能的主要因素;后者在挖掘的过程中不产生候选模式。为了更加高效地挖掘效用值最高的k个模式,一阶段算法TKHUP被提出,该算法在进行数据挖掘的过程中主要是通过四个有效策略来减少时间和空间的消耗。通过大量的实验数据表明,TKHUP在时间性能上优于其他top-k高效用模式挖掘算法。 High utility pattern mining is a fundamental research in data mining,in which more and more algorithms about top-k high utility pattern mining algorithms are proposed, where k refers to the number of high utility patterns that users need to mine. It can be classified into two types:two-phase algorithm and single-phase.algorithm. The former generated a huge number of candidates in mining process, which was the primary factor to decreasing the performance of algorithm; the latter mined top-k high utility patterns without candidate generation. To mine the k of the most valuable patterns more efficiently, this paper proposed a single-phase algorithm TKHUP. The proposed algorithm used four effective strategies to save time and space consumption during mining process. A large number of experiments indicates that the performance of TKHUP is the state-of-the-art top- k high utility mining algorithm on time.
出处 《计算机应用研究》 CSCD 北大核心 2017年第11期3303-3307,共5页 Application Research of Computers
基金 国家自然科学基金资助项目(61370108)
关键词 高效用模式 top-k模式挖掘 效用挖掘 数据挖掘 high utility pattern top-k pattern mining utility mining data mining
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  • 1潘云鹤,王金龙,徐从富.数据流频繁模式挖掘研究进展[J].自动化学报,2006,32(4):594-602. 被引量:34
  • 2Tseng V S,Shie B,Wu C,et al.Efficient algorithms for mininghigh utility itemsets from transactional databases[J].IEEE Transa-ctions on Knowledge and Data Engineering,2012(1); 1-10.
  • 3Li H,Huara H,Chen Y,et al.Fast and memory efficient miningof high utility itemsets in data streams[C]// Maui,HI,UnitedStates:Association for Computing Machinery,2008.
  • 4Ahmed C F,Tanbeer S K,Jeong B S,et al.Efficient treestructures for high utility pattern mining in incremental databa-ses[J],IEEE Transactions on Knowledge and Data Engineering,2009,21(12):1708-1721.
  • 5Tseng V S,Wu C W,Shie BE,et al.UP-Growth:An efficientalgorithm for high utility itemset mining[C]// Washington,DC,United States,2010.
  • 6Lin C W,Hong T P,Lan G C,et al.Mining high utility item-sets based on the pre-large concept[M],Advances in Intelli-gent Systems and Applications,2013:243-250.
  • 7Song W,Liu Y,Li J.Vertical mining for high utility itemsets[C]// Maui,HI,United States:Association for ComputingMachinery,2012.
  • 8Wu C W,Shie B,Tseng V S,et al.Mining top-K high utilityitemsets[C]// Washington,DC,United States,2012.
  • 9Liu J,Wang K,Fung B.Direct discovery of high utility item-sets without candidate generation[C]// Maui,HI,Unitedstates:Association for Computing Machinery,2012.
  • 10Liu M,Qu J.Mining high utility itemsets without candidategeneration[C]// Maui,HI,United states:Association forComputing Machinery,2012.

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