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Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models

Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models
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Author(s): Goran Klepac (Raiffeisenbank Austria Zagreb, Croatia)
Copyright: 2017
Pages: 29
Source title: Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0788-8.ch033

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Abstract

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive models as referent values for maximization value of probability that customers select/buy a product or service. Recognized results are used as a base for finding similar profiles between customers. The presented methodology has practical value for decision support in business, where information about customer profiles are valuable information for campaign planning and customer portfolio management.

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