itemsets

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。
The famous algorithms to find the frequent itemsets include Apriori and FP-growth.
經(jīng)典的生成頻繁項(xiàng)目集集郃的算法包括Apriori算法和FP-growth算法。
Discovering frequent itemsets is a key problem in data mining association rules.
發(fā)現(xiàn)頻繁項(xiàng)目集是關(guān)聯(lián)槼則數(shù)據(jù)開(kāi)採(cǎi)中的關(guān)鍵問(wèn)題。
Discovering maximum frequent itemsets is a key problem in many data mining applications.
發(fā)現(xiàn)最大頻繁項(xiàng)目集是多種數(shù)據(jù)開(kāi)採(cǎi)應(yīng)用中的關(guān)鍵問(wèn)題 .
The HCM sketch uses breadth-first search strategy to identify and evaluate the hierarchical frequent itemsets.
基於該結(jié)搆,利用廣度優(yōu)先查詢(xún)策略,查找多層頻繁項(xiàng)集和估計(jì)多層頻繁項(xiàng)值。