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Temporal Data Mining for Identifying Customer Behaviour Patterns

By Jurijs Čižovs, Tatjana Zmanovska and Arkādijs Borisovs

Abstract

This paper addresses the application of Data Mining technologies in the task of price formation and adjustment for the existing manufacturing plant through studying customer behaviour. The problem of fine price adjustment is especially topical for medium-size and large manufacturing plants. The research is aimed at developing a technique providing a validated recommendation or decision evaluation in the task of price adjustment. The introduced concept of sales volume behaviour profile is based on customer behaviour analysis. A sys-tem to frame and process multidimensional time-series is proposed and imple-mented. A practical result of the study is a software tool enabling the manager to obtain the prediction of the changes in sales volumes for the target decision using their behaviour profiles

Topics: Temporal Data Mining (DM), Multi-dimensional Time Series, Customer Behavior Pattern, Cluster Analysis
Publisher: IBaI Publishing
OAI identifier: oai:ortus.rtu.lv:5556
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