南京理工大学学报（自然科学版）
南京理工大學學報（自然科學版）
남경리공대학학보（자연과학판）
Journal of Nanjing University of Science and Technology
2015年
4期
420-425
,共6页
不确定数据%频繁项集%期望支持度%快速挖掘
不確定數據%頻繁項集%期望支持度%快速挖掘
불학정수거%빈번항집%기망지지도%쾌속알굴
uncertain databases%frequent itemsets%expected support%fast mining
针对CUF-growth算法中项集的期望支持度估算值过大,且挖掘过程中需要反复递归构造条件CUF-tree 导致挖掘效率降低这一问题,提出 UFIM-Matrix ( Uncertain frequent itemset mining-matrix)算法. 该算法不需要建立树结构,而是利用计算项集估算期望支持度的新方法和矩阵结构来产生规模更小候选项集,能在一定程度上减少计算开销,提高挖掘效率. 最后的实验结果也表明了新算法性能更优.
針對CUF-growth算法中項集的期望支持度估算值過大,且挖掘過程中需要反複遞歸構造條件CUF-tree 導緻挖掘效率降低這一問題,提齣 UFIM-Matrix ( Uncertain frequent itemset mining-matrix)算法. 該算法不需要建立樹結構,而是利用計算項集估算期望支持度的新方法和矩陣結構來產生規模更小候選項集,能在一定程度上減少計算開銷,提高挖掘效率. 最後的實驗結果也錶明瞭新算法性能更優.
침대CUF-growth산법중항집적기망지지도고산치과대,차알굴과정중수요반복체귀구조조건CUF-tree 도치알굴효솔강저저일문제,제출 UFIM-Matrix ( Uncertain frequent itemset mining-matrix)산법. 해산법불수요건립수결구,이시이용계산항집고산기망지지도적신방법화구진결구래산생규모경소후선항집,능재일정정도상감소계산개소,제고알굴효솔. 최후적실험결과야표명료신산법성능경우.
The CUF-growth algorithm gives an upper bound on the expected support of itemsets,but the estimate is too high. It has own bottleneck that needs to build conditional CUF-tree recursively in the mining process for getting candidate itemsets. According to the deficiency of the CUF-growth,the UFIM-Matrix( Uncertain frequent itemset mining-matrix) algorithm is proposed. This algorithm does not need to build a pattern tree while it generates smaller candidate sets by using a matrix structure and an improved method to calculate the upper bound of the expected support of itemsets. It can greatly reduce the cost of computing and improve the mining efficiency. The experimental results indicate the algorithm is more effective and efficient.
