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Theoretical analysis and experimental results suggest that FMAGMFI algorithm is efficient.

Discovering frequent itemsets is a key problem in data mining applications and the high cost of the process leads to the need for incremental data mining algorithms The difficulty of incremental updating frequent itemsets is analyzed and pointed out, and a fast algorithm FUFIA is put forward to find new frequent itemsets Finally, the analysis and discussion on this algorithm are also presented GAO Ming, LIU Xi-yu, SHENG Li Information Management Department, Shandong Normal University, Jinan 250014, China; An Improved Algorithm For Updating Frequent Itemsets[J]; Journal of Binzhou Teachers College;2005-03WANG Pei-ji1, ZHANG Shu-lin1, ZHAO Yu-lin2(1.

Firstly, each computer nodes compute local maximum frequent itemsets with DMFIA algorithm and local FP-tree.

Secondly, the center node combined local maximum frequent itemsets.

Center of Network Information, Baotou Medical College, Baotou 014010, China); Improvement and implementation of association rules mining based on recognizable matrix[J]; Journal of Lanzhou University of Technology;2012-01MENG Ren~1, SU Yi-Juan~2, ZHU Xiao-feng~3, ZHANG Ji-lian~3(1.

Finance Department, Guangxi Normal University, Guilin, Guangxi,541004, China;2.

Furthermore, this paper introduced FUFIA (fast updating frequent itemsets algorithm), which could get the new frequent itemsets through searching three-dimensional itemsets matrix when the database and the minimum support were changed.

Both theoretical analysis and experimental results showed the feasibility and effectiveness of the two algorithms.

fast updating frequent itemset-56fast updating frequent itemset-44

Only needed one pass to scan the database and did not create candidate itemsets, we could gain all the frequent itemsets.

Springer, Berlin, Heidelberg As far as we know, a little research of global maximum frequent itemsets had been done.

Therefore, a fast mining algorithm of global maximum frequent itemsets was proposed, namely, FMAGMFI algorithm.

Next, a brief conclusion and future works are proposed. The core of this method is the usage of a special data structure named frequent-pattern tree (FP-tree), which retains the itemset association information.

In simple words, this algorithm works as follows: first it compresses the input database creating an FP-tree instance to represent frequent items.




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