Shelf Layout With Integrating Data Mining And Multi-Dimensional Scaling

Thanks to information, communication and technological improvements in these days, data mining method are used to obtain significant results from very large data sets. In terms of businesses, decisionmaking in product design, placement, layout and so on issues are of vital importance. Association rules taking part in data mining topic is used so much especially in marketing research in the market basket. The Multi- Dimensional scaling (MDS) method is also frequently used for the positioning of products in the marketing field. MDS is measured similarities between products, units and so on according to the method of Euclidean space. Relations between products or units are visualized in two or three dimensions using MDS method according to the purpose. The aim of this study is to determine the product shelf layout using association rules according to the relationship map of the products generated by MDS. Together with the association rules (conviction ratios) used in data mining field, proximity coefficients between products were calculated and used in MDS analyze. Product groups were created by using MDS and proximity coefficient combinations made up between products. Shelf layout ensuring similar products in line with side by side was determined with the help of association rules. The applicability of the proposed method for products and alternative shelf layout was presented visually. 750 shopping and customers who purchase products in the same shelf made up the data of this study. In this study, placement of the products designed to maximize the benefit level for customers in terms of time and convenience.

[1]  A. Householder,et al.  Discussion of a set of points in terms of their mutual distances , 1938 .

[2]  Jian Wang,et al.  Development of the integrated fuzzy analytical hierarchy process with multidimensional scaling in selection of natural wastewater treatment alternatives , 2015 .

[3]  Rahul Dev Garg,et al.  Regional electricity consumption analysis for consumers using data mining techniques and consumer meter reading data , 2016 .

[4]  C. Coombs.  A theory of data. , 1965, Psychology Review.

[5]  Ibrahim Cil,et al.  Consumption universes based supermarket layout through association rule mining and multidimensional scaling , 2012, Expert Syst. Appl..

[6]  Mehmet Aksarayli,et al.  Malzeme Taşıma Odaklı Planlama için Üretim Sistemlerindeki Tezgah Yerleşim Düzenlerinin Benzetim Analizi ile Karşılaştırılması , 2009 .

[7]  Darshan M. Tank,et al.  Improved Apriori Algorithm for Mining Association Rules , 2014 .

[8]  George E. Tsekouras,et al.  Fuzzy rule base simplification using multidimensional scaling and constrained optimization , 2016, Fuzzy Sets Syst..

[9]  M. Hossein Safizadeh,et al.  Application of multidimensional scaling techniques to facilities layout , 1996 .

[10]  Hamidreza Esmalifalak,et al.  (Dis)integration levels across global stock markets: A multidimensional scaling and cluster analysis , 2015, Expert Syst. Appl..

[11]  Shirley Coleman,et al.  A Practical Guide to Data Mining for Business and Industry , 2014 .

[12]  Susan E.P. Bastian,et al.  Multidimensional scaling (MDS), cluster and descriptive analyses provide preliminary insights into Australian Shiraz wine regional characteristics , 2013 .

[13]  Feri Sulianta,et al.  Mining food industry's multidimensional data to produce association rules using apriori algorithm as a basis of business strategy , 2013, 2013 International Conference of Information and Communication Technology (ICoICT).

[14]  A. Raftery,et al.  Bayesian Multidimensional Scaling and Choice of Dimension , 2001 .

[15]  Q. Henry Wu,et al.  Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Zhiyuan Liu,et al.  Investigation of work zone crash casualty patterns using association rules. , 2016, Accident; analysis and prevention.

[17]  Mohammed Al-Maolegi,et al.  An Improved Apriori Algorithm for Association Rules , 2014, ArXiv.

[18]  Philip S. Yu,et al.  Association Rule Mining using Improved Apriori Algorithm , 2015 .

[19]  Robert Tibshirani,et al.  Supervised multidimensional scaling for visualization, classification, and bipartite ranking , 2011, Comput. Stat. Data Anal..

[20]  Igor Bisio,et al.  Data mining algorithms for communication networks control: concepts, survey and guidelines , 2016, IEEE Network.

[21]  B. Olatunji,et al.  Extracting body image symptom dimensions among eating disorder patients: the Profile Analysis via Multidimensional Scaling (PAMS) approach. , 2015, Body image.

[22]  Kensuke Okada,et al.  A Bayesian approach to modeling group and individual differences in multidimensional scaling , 2016 .

[23]  Witold Dzwinel,et al.  Interactive Data Mining by Using Multidimensional Scaling , 2013, ICCS.

[24]  David L. Olson,et al.  Advanced Data Mining Techniques , 2008 .

[25]  S. Keuchel,et al.  Deriving Constructs for Hierarchical Information Integration Experiments in Regional Public Transport , 2012 .

[26]  Joseph L. Zinnes,et al.  Theory and Methods of Scaling. , 1958 .

[27]  Cecilio Mar-Molinero,et al.  Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling , 2017 .

[28]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[29]  Gang Kou,et al.  Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches , 2015, Electron. Commer. Res. Appl..

[30]  J. A. Tenreiro Machado,et al.  Analysis of stock market indices through multidimensional scaling , 2011 .

[31]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[32]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[33]  Cheng-Hsiung Weng.  Identifying association rules of specific later-marketed products , 2016, Appl. Soft Comput..

[34]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[35]  Sooyoung Kim,et al.  Application of multidimensional scaling for marketing-mix modification: A case study on mobile phone category , 2009, Expert Syst. Appl..