A review on comparative performance analysis of associative classifiers
Amanat Ali · International Journal of ADVANCED AND APPLIED SCIENCES · 2017
In this study we provided comparative study of associative classifiers which can be exploited for the discovery of business rules from the huge structured and unstructured data that can be used in the business analytic.Associative classification is a hybrid approach combining the classification rules mining and association rules mining that are two important data mining tasks.There are various emerging classification problems in various domains of knowledge like medical data, images, audio, video and textual data.Associative Classification approaches are exploited in various fields for the classification purposes.We compare the selective associative classification methods namely CBA, CBA2, CMAR-C, CFAR-C, CPAR-C, and Fuzzy-FARCHD-C by exploiting the implementation of these methods in KEEL data mining tool on public datasets.Our experimental results reveals that the performance of the Fuzzy-FARCHD-C is promising than other methods in terms of accuracy.The performance of the associative classifiers drastically decreases on the datasets with higher number classes and attributes.