IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

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
View Sample PDF
Author(s): Goran Klepac (Raiffeisenbank Austria Zagreb, Croatia)
Copyright: 2015
Pages: 29
Source title: Handbook of Research on Swarm Intelligence in Engineering
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-4666-8291-7.ch013

Purchase

View Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models on the publisher's website for pricing and purchasing information.

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.

Related Content

Kamel Mouloudj, Vu Lan Oanh LE, Achouak Bouarar, Ahmed Chemseddine Bouarar, Dachel Martínez Asanza, Mayuri Srivastava. © 2024. 20 pages.
José Eduardo Aleixo, José Luís Reis, Sandrina Francisca Teixeira, Ana Pinto de Lima. © 2024. 52 pages.
Jorge Figueiredo, Isabel Oliveira, Sérgio Silva, Margarida Pocinho, António Cardoso, Manuel Pereira. © 2024. 24 pages.
Fatih Pinarbasi. © 2024. 20 pages.
Stavros Kaperonis. © 2024. 25 pages.
Thomas Rui Mendes, Ana Cristina Antunes. © 2024. 24 pages.
Nuno Geada. © 2024. 12 pages.
Body Bottom