CAJ | 학술논문

数据流最大频繁项集的项集数目相对很少并且已隐含所有的频繁项集，所以数据流中最大频繁项集的挖掘具有很好的时空效率并且有很大的意义，也受到了业界更多的关注。针对数据流最大频繁项集的挖掘，提出了在滑动窗口中基于矩阵的数据流最大频繁项集挖掘方法SWM-MFI，主要采用两个矩阵来存储数据信息：一个矩阵是事务矩阵，存储事务数据；一个矩阵是二项集矩阵，存放频繁2-项集。通过二项集矩阵扩展得到频繁k-项集，基于SWM-MFI算法挖掘出最大频繁项集。经过理论和实验证明该算法具有很好的时效性。
수거류최대빈번항집적항집수목상대흔소병차이은함소유적빈번항집，소이수거류중최대빈번항집적알굴구유흔호적시공효솔병차유흔대적의의，야수도료업계경다적관주。침대수거류최대빈번항집적알굴，제출료재활동창구중기우구진적수거류최대빈번항집알굴방법SWM-MFI，주요채용량개구진래존저수거신식：일개구진시사무구진，존저사무수거；일개구진시이항집구진，존방빈번2-항집。통과이항집구진확전득도빈번k-항집，기우SWM-MFI산법알굴출최대빈번항집。경과이론화실험증명해산법구유흔호적시효성。
The number of itemsets in data stream maximum frequent itemsets is relatively few and has implied all frequent itemsets, so mining data stream maximum frequent itemsets has better efficiency in time and space and has great significance. It has gotten more attention by the industry. In view of the data stream maximum frequent itemsets, this paper proposes a mining method called SWM-MFI based on matrix of data stream maximum frequent itemsets in sliding window. The method stores the data information using two Matrixes:one called transaction matrix stores the transaction data and the other one called 2-itemsets matrix stores frequent 2-itemsets. Frequent k-itemsets can be got through the 2-itemsets matrix and the maximum frequent itemsets can be mined based on the method of SWM-MFI. The theories and experiments show that the method is better on time efficiency.