Application of clustering algorithms to analyze the customer base of the store

Author:

Evdokimova Svetlana1,Zhuravlev Aleksandr1,Novikova Tatyana1

Affiliation:

1. Voronezh State University of Forestry and Technologies named after G.F. Morozov

Abstract

This paper analyzes the buyers of the BigCar store, which sells spare parts for trucks, using clustering methods. The algorithms of k-means, g-means, EM and construction of Kohonen networks are considered. For their implementation, the Loginom Community analytical platform is used. Based on sales data for 3 years, buyers are divided into 3 clusters by implementing the k-means, EM algorithms and building a self-organizing Kohonen network. An EM algorithm was also performed with automatic determination of the number of clusters and g-means, which divided buyers into 9 and 10 clusters. The analysis of the resulting clusters showed that the results of the k-means and Kohonen algorithms are better suited to increase sales efficiency.

Publisher

Infra-M Academic Publishing House

Subject

General Medicine

Reference11 articles.

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