Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Use a Learned Model: A Two-Stage Approach to Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
A review of associative classification mining
The Knowledge Engineering Review
A Novel Rule Weighting Approach in Classification Association Rule Mining
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Association Classification Based on Compactness of Rules
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
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Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. It usually spreads through the air and attacks low immune bodies. Human Immunodeficiency Virus(HIV) patients are more likely to be attacked with TB. It is an important health problem in India also. The application of association rule mining to classification has led to a new family of classifiers which are often referred to as Associative Classifiers (AC). An advantage of AC is that they are rule-based and if applied on medical datasets, lends themselves to an easier interpretation. It selects a small set of high quality rules and uses this rule set for prediction. This paper proposes classification of TB using classification based association(CBA) and classification based on multiple association rule(CMAR) techniques with our designed prototype model. It predicts the class label of unknown sample as Pulmonary Tuberculosis(PTB) or Retroviral Pulmonary Tuberculosis(RPTB) ie. TB along with HIV based on higher confidence rule.