Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets
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 r...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/973750 |
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| author | Sajid Mahmood Muhammad Shahbaz Aziz Guergachi |
| author_facet | Sajid Mahmood Muhammad Shahbaz Aziz Guergachi |
| author_sort | Sajid Mahmood |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a0d9a08503b94b958e5830f8d69385ca |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-a0d9a08503b94b958e5830f8d69385ca2025-02-03T06:42:18ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/973750973750Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent ItemsetsSajid Mahmood0Muhammad Shahbaz1Aziz Guergachi2Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, PakistanDepartment of Computer Science & Engineering, University of Engineering & Technology, Lahore, PakistanTed Rogers School of Information Technology Management, Ryerson University, Toronto, CanadaAssociation 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.http://dx.doi.org/10.1155/2014/973750 |
| spellingShingle | Sajid Mahmood Muhammad Shahbaz Aziz Guergachi Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets The Scientific World Journal |
| title | Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets |
| title_full | Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets |
| title_fullStr | Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets |
| title_full_unstemmed | Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets |
| title_short | Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets |
| title_sort | negative and positive association rules mining from text using frequent and infrequent itemsets |
| url | http://dx.doi.org/10.1155/2014/973750 |
| work_keys_str_mv | AT sajidmahmood negativeandpositiveassociationrulesminingfromtextusingfrequentandinfrequentitemsets AT muhammadshahbaz negativeandpositiveassociationrulesminingfromtextusingfrequentandinfrequentitemsets AT azizguergachi negativeandpositiveassociationrulesminingfromtextusingfrequentandinfrequentitemsets |