A comprehensive analysis on associative classification in medical datasets

Abstract

Association rule mining along with classification technique is capable of finding informative patterns from large data sets. Output of this technique is of the form if-then which is easy to understand for the end users and also for prediction. Termed as Associative Classification, it is having wide application on medical domain in diagnosing diseases and to analyze medical datasets which are unstructured, heterogeneous, incomprehensible and voluminous. Analysing these data allows physicians to predict the diseases as well as to take vital decisions. This paper presents a detailed study on associative classification and the phases of associative classification procedure. Several associative classification methods viz., CPAR, MMAC, CAR, CBA, CMAR, etc. along with their merits and demerits are also presented in a lucid manner.