电脑开发与应用
電腦開髮與應用
전뇌개발여응용
COMPUTER DEVELOPMENT & APPLICATIONS
2014年
6期
1-3
,共3页
Apriori算法%关联规则%数据挖掘%多排序%学位预警
Apriori算法%關聯規則%數據挖掘%多排序%學位預警
Apriori산법%관련규칙%수거알굴%다배서%학위예경
Apriori algorithm%association rules%data mining%multi-keyword sort%degree warning
分析了基于关联规则的数据挖掘技术原理，描述了经典的Apriori算法的原理及在实际应用中的弊端，并在此基础上运用精减频繁项集、运用多关键字排序重排频繁项集、压缩数据库方式以及算法中止条件方面对Apriori算法进行改进，并成功应用于高校学位预警系统中。
分析瞭基于關聯規則的數據挖掘技術原理，描述瞭經典的Apriori算法的原理及在實際應用中的弊耑，併在此基礎上運用精減頻繁項集、運用多關鍵字排序重排頻繁項集、壓縮數據庫方式以及算法中止條件方麵對Apriori算法進行改進，併成功應用于高校學位預警繫統中。
분석료기우관련규칙적수거알굴기술원리，묘술료경전적Apriori산법적원리급재실제응용중적폐단，병재차기출상운용정감빈번항집、운용다관건자배서중배빈번항집、압축수거고방식이급산법중지조건방면대Apriori산법진행개진，병성공응용우고교학위예경계통중。
This paper analyzes the principle of data mining techniques based on association rules, and describes the princilpe and disadvantages about the classic Apriori algorithm in practical application. The improved Apriori algorithm is gived that reducing frequent itemsets, rearranging frequent itemsets based on multi-keyword sort ,compressing database and abort Apriori algorithm according to frequent itemsets number. The improved Apriori algorithm is successfully used in early warning system about college degree.
