Abstract-Available approaches for Association Rule Mining (ARM) generates a large number of association rules, these rules may be trivial and redundant and also such rules are difficult to manage and understand for the users. If we consider their complexity, then it consumes lots of time and memory. Sometimes decision making is impossible for such kinds of association rules. An inference approach is required to resolve this kind of problem and to produce an interesting knowledge for the user. In this paper, we present an inference mechanism framework for ARM, which would be capable enough for resolving such problems, it would also predict future possibilities using Markov predictor by analyzing available fact and inference rules.

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The association rule mining is most popular and real time applicable approach for finding interesting relations between items. Many of the ARM (Association rule Mining) approaches are well investigated in the literature, but it generates large number of association rules. If the dataset size is larger, then huge rules may occur, often it is a critical situation where decision making is difficult or unattainable because knowledge is not directly present in frequent patterns. This paper presents an improved AIRM (Association Inference Rule Mining) algorithm where fuzzy logic based C-Means clustering concept has been adopted to discover inference knowledge from frequent patterns. For experimental study, we apply this approach on a clinical dataset of 1000 patients, contained symptoms having different diseases. Proposed approach follows three phase procedure in order to achieve inference knowledge, in the first phase preprocess the data, second phase apply the ARM and finally FIS has to be applied to discover inference knowledge by matching inference rules and put the data in the appropriate class on the basis of their matching degree. The new approach is efficient and outperforms as compared to a previous AIRM algorithm in order to match inference rules and knowledge discovery process.

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.