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Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models (CROSBI ID 58347)

Prilog u knjizi | izvorni znanstveni rad

Klepac, Goran Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models // Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications (3 Volumes) / Information Resources Management Association (USA) (ur.).: IGI Global, 2017. str. 864-892

Podaci o odgovornosti

Klepac, Goran

engleski

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

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.

Data Mining

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Podaci o prilogu

864-892.

objavljeno

Podaci o knjizi

Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications (3 Volumes)

Information Resources Management Association (USA)

IGI Global

2017.

9781522507888

Povezanost rada

Povezane osobe




Informacijske i komunikacijske znanosti