Shelf Layout With Integrating Data Mining And Multi-Dimensional Scaling

OpenAlex · İngilizce

Thanks to information, communication and technological \nimprovements in these days, data mining method are used to obtain \nsignificant results from very large data sets. In terms of businesses, decisionmaking in product design, placement, layout and so on issues are of vital \nimportance. Association rules taking part in data mining topic is used so \nmuch especially in marketing research in the market basket. The MultiDimensional scaling (MDS) method is also frequently used for the \npositioning of products in the marketing field. MDS is measured similarities \nbetween products, units and so on according to the method of Euclidean \nspace. Relations between products or units are visualized in two or three \ndimensions using MDS method according to the purpose. The aim of this \nstudy is to determine the product shelf layout using association rules \naccording to the relationship map of the products generated by MDS. \nTogether with the association rules (conviction ratios) used in data mining \nfield, proximity coefficients between products were calculated and used in \nMDS analyze. Product groups were created by using MDS and proximity \ncoefficient combinations made up between products. Shelf layout ensuring \nsimilar products in line with side by side was determined with the help of \nassociation rules. The applicability of the proposed method for products and \nalternative shelf layout was presented visually. 750 shopping and customers \nwho purchase products in the same shelf made up the data of this study. In \nthis study, placement of the products designed to maximize the benefit level \nfor customers in terms of time and convenience.