Enhanced Quantitative Association Rule Mining Techniques For Prediction Analysis in Betathalesemia's Patients
Siji P. D · 2014
This paper is a study into a fuzzy association rule mining model for better prediction performance in a particular medical database. The model (the FCM-MSMM- Apriori model) integrates multi membership and multiple support approach for Betathalasemia disease for performance prediction. The idea of multiple membership functions is measured to offer fuzzy quantitative information, selecting best one out of the different membership functions. Betathalasemia data which usually has a quantitative structure in nature. Traditional data mining algorithms such as association rules, when applied alone, often produce uncertain and unreliable results. The new algorithm focuses on characteristics of the variety structure of data, and the association rules of data causes can be calculated more accurately and in higher rates. The Novel application of Multi Support Multiple membership function to the attributes according to their nature gives better prediction and good performance analysis