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Trends in Bioinformatics
  Year: 2011 | Volume: 4 | Issue: 1 | Page No.: 35-46
DOI: 10.3923/tb.2011.35.46
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Extracting Association Rules from Hiv Infected Patients’ Treatment Dataset

K. Rameshkumar

In recent days, the data mining techniques are fascinatingly applied in healthcare domain. It proved that these techniques are suitable to extract knowledge from medical domain. Association Rule Mining (ARM) is mined valuable information from large voluminous databases. But most of ARM algorithms are mined many uninteresting or unrelated knowledge. We proposed new n-cross validation based Apriori (nVApriori) algorithm to mine domain irrelevant rules. Acquired Immuno Deficiency Syndrome (AIDS) is a challenge infection in the field of medical domain. This work proposes a new dataset for AIDS/HIV infected patients’ case history. The data were collected from Midwest clinics, London. The nVApriori algorithm applies with the proposed dataset. It mines many interesting rules, provides much useful information to domain experts. The proposed algorithm is performed better than traditional Apriori, most interesting rule mining algorithm, Non redundant rule mining algorithm.
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  •    Knowledge Discovery Process: Guide Lines for New Researchers
  •    Improving the Performance of Association Rule Mining Algorithms by Filtering Insignificant Transactions Dynamically
  •    Design of Algorithm for Frequent Pattern Discovery Using Lattice Approach
  •    Measuring the Interestingness of Classification Rules
How to cite this article:

K. Rameshkumar , 2011. Extracting Association Rules from Hiv Infected Patients’ Treatment Dataset. Trends in Bioinformatics, 4: 35-46.

DOI: 10.3923/tb.2011.35.46






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