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The Scientific World Journal
Volume 2014, Article ID 973750, 11 pages
Research Article

Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets

1Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan
2Al-Khawarizmi Institute of Computer Sciences, UET, Lahore, Pakistan
3Ted Rogers School of Information Technology Management, Ryerson University, Toronto, Canada

Received 17 February 2014; Revised 27 March 2014; Accepted 1 April 2014; Published 18 May 2014

Academic Editor: Daniel D. Sánchez

Copyright © 2014 Sajid Mahmood et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, that is, frequent itemsets. These problems include infrequent itemsets discovery and generation of accurate NARs, and their huge number as compared with positive association rules. In medical science, for example, one is interested in factors which can either adjudicate the presence of a disease or write-off of its possibility. The vivid positive symptoms are often obvious; however, negative symptoms are subtler and more difficult to recognize and diagnose. In this paper, we propose an algorithm for discovering positive and negative association rules among frequent and infrequent itemsets. We identify associations among medications, symptoms, and laboratory results using state-of-the-art data mining technology.