FSA-Red Algorithm, was introduced by Feri Sulianta in International Conference of Information and Communication Technology (ICOICT), Indonesia, Bandung, Wednesday, March 20, 2013 when he delivered presentation with the theme topics Mining food industrys multidimensional data to produce association rules using apriori algorithm as a basis of business stratgey. The Algorithm is used for data reduction or preprocessing to minimize the attribute to be analyzed. The goal is to make strong association rules using data mining technics related to the data which is reduced . The data preprocessing in FSA-Red performed a few of reduction techniques such as attribute selection, row selection and feature selection. Row selection has done by deleting all signed record which related to the attribute which need to be analyzed. Feature selection will remove all the unwanted attribute, ended with attribute selection to eliminate the non value attributes which is no need to be included.. The Idea base on the justification no matter the reduction has done the reduction procedure have to consider the presence of the other information in all dataset, so that the reduction should be done systematically consider the linkages between attributes. After the reduction process there would be only the in instances in the small scale with integrity by mean no information lost among the attribute in every selective instance.
The flexibility according to the FSA-Red Algorithm is the way attribute is chosen, there is no limitation to exclude the attribute, by mean any kind of attribute can be chose as a basis of reduction process even though there would be the attribute which is not the best compare to the others. This is the benefit from the reduction procedure which might result rich association patterns of the data.