Two Novel Pioneer Objectives of Association Rule Mining for high and low correlation of 2-varibales and 3-variables

Abstract

:Association rule generation is a significant research area of data mining, which find out the relation between the set of items . Significant association rule mainly based on two objectives -support and confidence. Some other metrics are also available to evaluate the goodness, effectiveness and interestingness of an association rule. Therefore, the association rule mining problem can be treated as multi-objective optimization problem. In this paper, we discuss the various objectives and their limitation. It is found that, each and every objective are not suitable in every situation. Other than this , most of the objectives are defined for 2-variables only. Simultaneously, in certain situation correlation analysis does not show the positive and negative correlation between items. Authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. Through numerical analysis it is found that proposed objective clearly indicate about the positive and negative correlation among items. These objectives also gives appropriate solution in those cases, where previously defined objectives have some limitations. Simultaneously it also works in Simpson's paradox situation successfully.

An Empirical Algorithm for High and Low Correlative Association Rule Mining

“…Various authors proposed the variation of association rule mining approach through positive and negative association rule [21][22] rare association rule [23] and association rules by using various objectives [18] including right interesting measures [24].…”