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Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets
Joint Authors
Mahmood, Sajid
Shahbaz, Muhammad
Guergachi, Aziz
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-18
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Mahmood, Sajid& Shahbaz, Muhammad& Guergachi, Aziz. 2014. Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051822
Modern Language Association (MLA)
Mahmood, Sajid…[et al.]. Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1051822
American Medical Association (AMA)
Mahmood, Sajid& Shahbaz, Muhammad& Guergachi, Aziz. Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051822
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051822