Generalized Association Rules for ER Models by Using Mining Operations on Fuzzy Datasets

Today the business world is functioning in an ultra-competitive environment. However, the business units have realized that the key to survival and sustainability of the utilization of the data set which is obtained from various business processes. In other words, it means that the competitiveness is achieved by means of processing the dataset and ensuring that the trends and patterns provide insight into the decision making process. However, decision making is complex and complicated as several factors need to be taken into consideration. These factors give rise to the concept of the fuzzy datasets. The fuzzy datasets further narrow down the scope of the trends and patterns so that near accurate decisions can be obtained.

The paper seeks to address the issues of the fuzzy datasets in terms of bringing in maturity in the decision making process by ensuring that the association rule mining processes are able to bring in more accuracy and precision in the decision making procedures. Further, the paper seeks to address the issues of the entity relationship modelling that exists in the database tables and the means and mechanism deployed to overcome the issues and challenges posed by ER modelling. The proposed study aims to extend the existing algorithms comprising of Extended Apriori and Apriori star to determine a new algorithm. The contribution of the study results in an attempt to standardize algorithms for finding the most appropriate result from tables comprising of fuzzy data.