Title :
Mining opened frequent itemsets to generate maximal Boolean association rules
Author :
Jiang, Baoqing ; Han, Chong ; Li, Lingsheng
Author_Institution :
Inst. of Data & Knowledge Eng., Henan Univ., Kaifeng, China
Abstract :
Lots of association rules may be generated in the process of association rules minging. It leads to users hard to find important information they needed. The maximal Boolean association rules have the advantages that these rules contain a small number and don´t lose the rules´ information. Thereby it increased the efficiency of the users´ analysis about the rules and saved the storage space. Opened frequent itemsets and closed frequent itemsets can be used to mine the maximal Boolean association rules. In this paper, we analyse the property of maximal Boolean association rules and propose an algorithm of mining opened frequent itemset. Finally, we verify this algorithm by an example.
Keywords :
Boolean functions; data mining; closed frequent itemsets; maximal boolean association rules; opened frequent itemsets mining; Algorithm design and analysis; Association rules; Data mining; Diseases; Explosions; Itemsets; Knowledge engineering; Transaction databases;
Conference_Titel :
Granular Computing, 2009, GRC '09. IEEE International Conference on
Conference_Location :
Nanchang
Print_ISBN :
978-1-4244-4830-2
DOI :
10.1109/GRC.2009.5255112