An Inference Mechanism Framework for Association Rule Mining

Abstract

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.

A Fuzzy Inference Approach for Association Rule Mining

“…The algorithm Association Fuzzy Inference Rule Mining (AFIRM) presented as the extension of AIRM [5], by adopting the concept of fuzzy C-means clustering to match the facts, because accurate fact matching may drop many interesting facts. To overcome this problem AFIRM algorithm proposed in this paper which outperform as comparable to AIRM algorithm [5] from outcome perspective. Figure-3 shows a schematic representation of fuzzy C-means clustering based inference mechanism for association rule mining to discover inference knowledge.…”

“…An inference mechanism framework for association rule mining proposed in [5] by K. Chaturvedi et al This paper presents a theoretical and numerical study on association rule based inference mechanism for discovering knowledge from a medical dataset where patient dataset has been used to discover highly effected disease in a particular time slice. It also presents an algorithm AIRM to achieve the appropriate objective; this paper is an extension of AIRM to optimize the algorithm proposed in [5]. A fuzzy association based classification presented in [3] for high dimensional problems with genetic rule selection.…”

“…This information cannot be discovered just by applying traditional ARM approaches; here fuzzy logic can play an essential role to discover inference knowledge by using of additional concept of forward inference system. In our previous framework [5] by K. Chaturvedi et al an identical inference mechanism framework has presented for association rule mining, which follows five step procedure to discover inference knowledge from frequent patterns, here the fact matching process reflects some kind of weakness due to their exact fact matching process. This process may drop many of interesting patterns (frequent symptoms).…”

Development of a mobile expert system for the diagnosis on motorcycle damage using forward chaining algorithm

“…Therefore, this allows for easy-to-use interfaces which allow the system to operate without extensive training for non-technical users [5], [6]. Various systems also explain the output generated or suggest alternatives to choose from as requested by the user [18].…”