CAJ | 학술논문

研究频繁项集模式挖掘优化问题。传统的挖掘算法常产生大规模的候选项集，并且反复扫描数据库，导致频繁项集挖掘时间过长，空间效率太低。为了改进频繁项集挖掘时时间与空间效率低的问题，提出一种高效频繁项集挖掘算法CPT-Mine。此算法利用编码模式树存储事务数据库中的频繁项集信息，构建FP数组，加快产生频繁项集，引入CPT-Mine算法，快速地挖掘数据库中所包含的频繁项集，无需递归构造条件模式树，只需两次扫描数据库即可生成所有频繁项集。最后的实验证明了该算法能缩短挖掘时间3~10 s，空间效率提高43%。
연구빈번항집모식알굴우화문제。전통적알굴산법상산생대규모적후선항집，병차반복소묘수거고，도치빈번항집알굴시간과장，공간효솔태저。위료개진빈번항집알굴시시간여공간효솔저적문제，제출일충고효빈번항집알굴산법CPT-Mine。차산법이용편마모식수존저사무수거고중적빈번항집신식，구건FP수조，가쾌산생빈번항집，인입CPT-Mine산법，쾌속지알굴수거고중소포함적빈번항집，무수체귀구조조건모식수，지수량차소묘수거고즉가생성소유빈번항집。최후적실험증명료해산법능축단알굴시간3~10 s，공간효솔제고43%。
The frequent itemsets mining optimization model is researched. Traditional mining is often produce large-scale candidate itemsets, and repeatedly scanning database, the time is too long, lead to frequent itemsets mining space efficien-cy is too low. Frequent itemset mining is to improve the problem of low efficiency of time and space, put forward an efficient algorithm for mining frequent itemsets CPT-Mine. The algorithm using encoding scheme tree store information of frequent itemsets of transaction databases construction of FP array, to speed up the produce frequent itemsets, the introduction of CPT-Mine algorithm, fast, frequent itemsets mining database contains no recursive model tree structure conditions, only two times of scanning database can generate all frequent itemsets. The experiment proves that this algorithm can shorten the mining time 3~10 s, space efficiency increased by 43%.