Unsupervised Learning Methods as Tools for Discovering Relationships Within Data

Association Rules and Linear Regression are unsupervised learning methods. There is no “prediction” performed but is used to discover relationships within the data. The goal with Association rules is to discover interesting relationships among the variables and the definition of interesting depends on the algorithm used for the discovery. Association Rules are specifically designed for in-database mining over transactions in databases. Linear regression is used to predict a continuous value as a linear or additive function of other variables. In this paper we take case examples to see the relationships within data for efficiency of these two learning methods of association and linear regression.