DM Data Mining Based on Improved Apriori Algorithm

Association rules are the key technology in data mining; it has a very broad applying foreground in many industries. An improved association rules algorithms based on Apriori was proposed in this paper. And it will be used in direct mail data mining. By analyzing the normative database of users’ sets, we can get item set which satisfy the minimal support degree, and form the rule set. We can get more accurate DM data mining results than other methods by testing the post DM database. Experiments indicate the validity of the method.

[1]  Li Jun,et al.  An Improved Apriori Algorithm Based On the Boolean Matrix and Hadoop , 2011 .

[2]  Andy Novobilski,et al.  Mining Bayesian Networks to Forecast Adverse Outcomes Related to Acute Coronary Syndrome , 2004, FLAIRS Conference.

[3]  Behrouz Minaei-Bidgoli,et al.  Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence , 2011, Expert Syst. Appl..

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

[5]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[6]  Ye Liang,et al.  Discovery of Association Rules between TCM Properties in Drug Pairs by Association Mining between Datasets and Probability Tests , 2010 .

[7]  Sebastián Ventura,et al.  Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules , 2011, Knowledge and Information Systems.

[8]  Shu-Hsien Liao,et al.  Mining customer knowledge for tourism new product development and customer relationship management , 2010, Expert Syst. Appl..

[9]  Jiayi Zhou,et al.  Parallel TID-based frequent pattern mining algorithm on a PC Cluster and grid computing system , 2010, Expert Syst. Appl..

[10]  Shu-Hsien Liao,et al.  Ontology-based data mining approach implemented for sport marketing , 2009, Expert Syst. Appl..

[12]  Jiawei Han,et al.  Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes , 1997, KDD.

[13]  Jon M. Kleinberg,et al.  Segmentation problems , 2004, JACM.

[14]  W. Gibler,et al.  It's about time: the evolution of acute coronary syndrome evaluation in the emergency department. , 2002, Annals of emergency medicine.

[15]  W. Baxt,et al.  A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain. , 2002, Annals of emergency medicine.