Quantitative association rules over incomplete data

This paper explores the use of principle component analysis (PCA) to estimate missing values during the mining of quantitative association rules. An example of such association may be “15% of customers spend $100-$300 every month will have two cable outlets at home”. In our algorithm, instead of imputing missing values before the mining process, we propose to integrate the imputation step within the process. The idea is to reduce the unnecessary imputation effort and to improve the overall performance. First, only attributes with enough support counts and with missing values are required to perform imputations. Thus, effort will not be wasted on unimportant attributes. Further, rather than estimating the actual value of a missing data, the possible range of the value is guessed. This will not affect the resultant quantitative association rules much but will cut down the guessing effort