Itemset

基本解釋

The problem of fuzzy constraint in frequent itemset mining is studied.
摘要研究頻繁項(xiàng)集挖掘中的模糊約束問(wèn)題。
Setting this value to 0 specifies that there is no limit to the size of the itemset.
將該值設(shè)置爲(wèi)0將指定對(duì)項(xiàng)集的大小沒(méi)有限制。
Grahne G,Zhu Jian-fei.Fast algorithms for frequent itemset mining using FP-Trees[J].IEEE Transactions on Knowledge and Data Engineering,2005,10:1347.
胥桂仙;高旭;於紹娜;關(guān)聯(lián)槼則算法在中文文本挖掘中的應(yīng)用研究[J].;中央民族大學(xué)學(xué)報(bào);2004;13(4):332-338
For example, if every itemset contains a specific state of an attribute, a rule that predicts state is trivial, even though the probability is very high.
例如,如果每個(gè)項(xiàng)集都包含屬性的某個(gè)特定狀態(tài),那麼,即使概率非常高,預(yù)測(cè)狀態(tài)的槼則也竝不重要。
Setting this value to a whole number greater than 1 specifies the minimum number of cases as the absolute number of cases that must contain the itemset.
將該值設(shè)置爲(wèi)大於1的整數(shù)將指定最小事例數(shù)作爲(wèi)必須包含項(xiàng)集的事例的絕對(duì)數(shù)。