Authors: Raj Singh; Tom Johnsten; Vijay V. Raghavan; Ying Xie
Addresses: School of Science and Computer Engineering, University of Houston Clear Lake, Houston, TX 77058, USA. ' School of Computer and Information Sciences, University of South Alabama, Mobile, AL 36688, USA. ' Center of Advanced Computer Studies, University of Louisiana, Lafayette, LA 70504, USA. ' Deptartment of Computer Science and Information Systems, Kennesaw State University, Kennesaw, GA 30144, USA
Abstract: A pattern discovered from a collection of data is usually considered potentially interesting if its information content can assist the user in their decision making process. To that end, we have defined the concept of potential interestingness of a pattern based on whether it provides statistical knowledge that is able to affect one|s belief system. In this paper, we introduce two algorithms, referred to as All-Confidence based Discovery of Potentially Interesting Patterns (ACDPIP) and ACDPIP-Closed, to discover patterns that qualify as potentially interesting. We show that the ACDPIP algorithm represents an efficient alternative to an algorithm introduced in our earlier work, referred to as Discovery of Potentially Interesting Patterns (DAPIP). However, results of experimental investigations also show that the application of ACDPIP is limited to sparse datasets. In response, we propose the algorithm ACDPIP-Closed designed to effectively discover potentially interesting patterns from dense datasets.
Keywords: data mining; potential interesting patterns; positive patterns; negative patterns; association rules; closed frequent itemsets; pattern discovery.
International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2011 Vol.2 No.2, pp.107 - 122
Available online: 26 Oct 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article