A Partial Weighted Utility Measure for Fuzzy Association Rule Mining

Abstract

Background/Objectives: Association rules are generated from frequent item set by Association mining. The generation of frequent item set makes a great impact on decision making. The objective of the work is to introduce a new measure called SUF (Skill Utility Factor) to extract meaningful hidden item set and develop a hybrid algorithm FPWUM (Fuzzy Partial Weighted Utility Mining) for decision making. Methods/Statistical Analysis: The traditional measures support and confidence is augmented with SUF which can be useful for Human resource personnel to easily predict the work-force calibre in an organization. Using different methods like association rule mining, fuzzy logic and weighted utility mining has improved the prediction of attributes relations efficient and faster. Findings: The FPWUM extracts more efficient hidden frequent item sets through which many new and interesting rules are generated. Since the application of attribute’s weight are handled wisely and improvising factor is used only for hidden item set the model process time is reduced fairly. The idea of integrating the conventional measures and the SUF is a unique technique. The approach works well on real time dataset compared to the conventional models. The comparative result shows the algorithm’s ability. Improvements/ Applications: The algorithm uses predefined weighting scheme. It can be enhanced by using dynamic intelligent weighting factor.