Abstract: Association rule mining has wide variety of research in the field of data mining, many of association rule mining approaches are well investigated in literature, but the major issue with ARM is, huge number of frequent patterns cannot produce direct knowledge or factual knowledge, hence to find factual knowledge and to discover inference, we propose a novel approach AFIRM in this paper followed by two step procedure, first is to discover frequent pattern by Appling ARM algorithm and second is to discover inference by adopting the concept of Fuzzy c-means clustering, for performance analysis, we apply this approach on a clinical dataset (contained symptoms information of patients) and we got highly effected disease in a couple of months or in a session as hidden knowledge or inference.

Kapil Chaturvedi, Dr. Ravindra Patel and Dr. D.K. Swami. “Fuzzy C-Means based Inference Mechanism for Association Rule Mining: A Clinical Data Mining Approach”. International Journal of Advanced Computer Science and Applications (IJACSA) 6.6 (2015). http://dx.doi.org/10.14569/IJACSA.2015.060615

@article{Chaturvedi2015,
title = {Fuzzy C-Means based Inference Mechanism for Association Rule Mining: A Clinical Data Mining Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060615},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060615},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {6},
author = {Kapil Chaturvedi and Dr. Ravindra Patel and Dr. D.K. Swami}
}