Segmentation of Tourist Interest on Tourism Object Categories by Comparing PSO K-Means and DBSCAN Method

The purpose of the work is to develop an algorithm for analyzing the customer base of a trade organization, which allows customers to be divided into groups depending on their activity. In the future, by taking into account the preferences of each group of clients, it will be possible to increase the efficiency of working with clients. The authors use ABC-XYZ analysis and clustering methods in the algorithm, which make it possible to determine the most active customers who bring in the most profit. The ABC-XYZ analysis method divides buyers into groups depending on the amount and frequency of purchases, clustering methods combine the original objects into clusters based on similar characteristics. According to the algorithm presented in the work, RapidMiner Studio system has developed scenarios for analyzing the client base of a trade organization. The ABC-XYZ analysis method showed buyers who are worth paying special attention to, since their absence will lead to losses. The cluster analysis used the k-means methods, which divided the initial data set into 3 clusters, g-means and Expectation-Maximization algorithm, in which the number of clusters was not specified. The following characteristics were used: the average amount of purchases per year, the average number of unique products purchased per year, the average number of purchases per year, the number of years that the buyer cooperates with the store, the year of the last purchase. The g-means method divided the buyers into 3 clusters, and the EM algorithm into 10. The combined ABC-XYZ analysis and k-means algorithm showed the best results of customer separation, allowing an individual approach to work with customers of each group.

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Using clustering methods to analyze sales of auto parts at a truck service station

Evdokimova,

Novikova,

Novikov

2023

Modeling of Systems and Processes

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Clustering methods are widely used to divide goods into groups depending on sales volumes in order to build an optimal purchasing planning and inventory management strategy. Cluster analysis methods do not provide an unambiguous partition of the original set of objects, therefore, in the work, existing clustering methods were analyzed to study sales of auto parts at truck service stations. To solve the problem, the following methods were chosen: k-means, hierarchical agglomerative clustering and DBSCAN. Before using the k-means method, the elbow method found the optimal number of clusters. The DBSCAN method is based on object density and automatically determines the number of clusters. The initial data for cluster analysis was information on sales of spare parts at truck service stations for 3 years; clustering was applied to data by year. The DBSCAN algorithm showed unsatisfactory results, since most of the goods (86%) were identified in one cluster, while others contained units of goods. The k-means method gave the best partitioning result, each group has a different volume. The distribution of goods in clusters changes over three years, so managers should study the change in the affiliation of goods to one group or another. The obtained clustering results will help determine the real needs of spare parts at truck service stations and build an optimal procurement strategy.

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Segmentation of Tourist Interest on Tourism Object Categories by Comparing PSO K-Means and DBSCAN Method

Cited by 3 publications

References 27 publications

A spatial distance-based spatial clustering algorithm for sparse image data

A spatial distance-based spatial clustering algorithm for sparse image data

Algorithm for analyzing the customer base of a trade organization

Using clustering methods to analyze sales of auto parts at a truck service station

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