Mining Multidimensional Fuzzy Association Rules from a Normalized Database

Mining association rules is one of the important tasks in the process of data mining application. In general, the input as used in the process of generating rules is taken from a certain data table by which all the corresponding values of every domain data have correlations one to each others as given in the data table. A problem arises when we need to generate the rules expressing the relationship between two or more domains that belong to several different tables in a normalized database. To overcome the problem, before generating rules it is necessary to join the participant tables into a general table by a process called Denormalization.. This paper shows a process of mining Multidimensional Fuzzy Association Rules from a normalized database. The process consists of two sub-process, namely sub-process of join tables (Denormalization) and sub-process of mining fuzzy rules. In general, some parts of mining the fuzzy association rules has been discussed in our previous papers [3,4,5,6].

1. Introduction

Association rule finds interesting association or correlation relationship among a large data set of items [1], [9]. The discovery of interesting association rules can help in decision making process.