USING THE K-MEANS CLUSTERING ALGORITHM FOR IMPROVING THE TEMPORAL STATITICS OF COMMERCIAL PROPOSALS VIEWS
Anomalies are considered as not typical and rare values, which decrease accuracy in date significant. Such values would generally cause inaccuracy in date analysis results, so they must be deleted. The article proposes to use the k-means clustering method in order to solve practical problems of data processing for displaying the temporal statistics in the B2B sector. The B2BFamily service for sending and tracking commercial offer represents the subject area and the data source. The article also proposes to remove anomalies and display more adequate temporal view statistics about the average time of the commercial offer slide review. That will help the sales manager to adjust the strategy of communication with customers. Finally, the authors discuss the results and trends of the study further development.