Maximum Frequent Itemsets Discovery Algorithm Based on Granular Computing

Different traditional frequent set discovery algorithms are applicable to data with different characteristics. Based on the summary and understanding of the current status, this paper proposes a maximum frequent itemsets discovery algorithm based on granular computing (GrGMFI). The algorithm is divided into the following stages: (1) It needs to scan the database and discover the frequent items and their corresponding bitmaps; (2)Establishing the directed hierarchy graph; (3)Mining the maximum frequent itemsets on the directed hierarchical graph. Comparing this algorithm with Mafia algorithm, we find that it has high efficiency both for dense data sets and sparse data sets.

[1]  Qiang Wu,et al.  Efficiently Mining Maximal Frequent Itemsets Based on Digraph , 2014 .

[2]  Kale Sarika Prakash,et al.  Efficient execution of data warehouse query using look ahead matching algorithm , 2016, 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT).

[3]  C. Sathya.  GenMax with Index Technique for Pruning Bounded Frequent Itemsets , 2014 .

[4]  Ömer M. Soysal,et al.  Association rule mining with mostly associated sequential patterns , 2015, Expert Syst. Appl..

[5]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[6]  Beizhan Wang,et al.  Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data , 2017, IEEE Transactions on Industrial Informatics.

[7]  Hongjuan Wu,et al.  A frequent itemset mining algorithm based on composite granular computing , 2018, J. Comput. Methods Sci. Eng..

[8]  Johannes Gehrke,et al.  MAFIA: A Performance Study of Mining Maximal Frequent Itemsets , 2003, FIMI.

[9]  Unil Yun,et al.  Incremental mining of weighted maximal frequent itemsets from dynamic databases , 2016, Expert Syst. Appl..

[10]  Yang Chuan-jian.  Mining algorithm for maximal frequent itemsets based on improved FP-tree , 2012 .

[11]  Dharavath Ramesh,et al.  PPS: Parallel Pincer Search for Mining Frequent Itemsets Based on Spark , 2016, SoCPaR.

[12]  Pankaj Kumar,et al.  REDUCTION OF NUMBER OF ASSOCIATION RULES WITH INTER ITEMSET DISTANCE IN TRANSACTION DATABASES , 2012 .

[13]  Ramesh C Agarwal,et al.  Depth first generation of long patterns , 2000, KDD '00.

[14]  Ming-Yen Lin,et al.  High utility pattern mining using the maximal itemset property and lexicographic tree structures , 2012, Inf. Sci..

[15]  Xiaojun Cao.  An Algorithm of Mining Association Rules Based on Granular Computing , 2012 .