Frequent Itemsets: Fuzzy Data from Multiple Datasets

Abstract

The application of association rule mining plays crucial role in the operations of data warehousing and data mining. To support this argument, the paper proposes a model having fuzzy taxonomic structure at the backend while retrieving frequent itemsets from the database which are organized in the form of star schema tabular database structures. The objective of the study generate new algorithm from the existing algorithm which deploy fuzzy association rule based mining operations in the databases having ER models. The focal point of the paper is centered on the extraction of rules pertaining to linguistic algorithm at multi level structures from several databases in the form tables so as to understand the performance of these extracted data item sets. An example at the end outlines the working of the proposed data mining algorithm which can be used to arrive at the multi level fuzzy rules which pertains to association mining algorithms in an easy and an effective manner.