An Ameliorating FP-growth Algorithm Based on Patterns-matrix
Zhenyu Liu · Journal of Xiamen University · 2005
The discovery of association rules is an very important aspect in data mining.There are Apriori and FP-growth algorithms among mining association rules algorithms.It has been proven that FP-growth algorithm is better than Apriori algorithm.But for very large databases there exists some big deficiencies in both algorithms,because two or more scans for the databases have to be done.It is also difficult to handle updating association rules in the cases including modifying support and inserting new data into the database.So,in the present paper,an ameliorating FP-growth algorithm based on patterns-matrix is presented which scans at most one for the database.Especially in updating problem,it needn't scan the database again.It indicates that the ameliorating algorithm is better than FP-growth algorithm through experience.The ameliorating algorithm sharply reduces the time come while mining those big datasets.