Learning Results
This course aims to equip students for instrumental and analytical capabilities in the field of Customer Relationship Management (CRM), namely to know the major concepts of Data Mining, encourage critical thinking and ability to select the techniques of preparation and pre-processing data and know the operation,
capabilities and limitations of the tools studied (k-means, self-organizing maps, market basket analysis, decision trees and multi-layer perceptrão).
Program
1.The context of data mining
2. Data mining as a tool for CRM
3. The methodological process of data mining
4. Problem definition and data collection
5. Preparation and pre-processing data
6. Techniques of data mining for CRM
1. Segmentation
2. Clustering Tools
3. Cluster analysis (k-means and self organizing maps)
4. Market basket analysis
5. Predictive modeling tools and Scoring
6. Decision trees
7. Neural networks
7. Analysis of case studies
Internship(s)
NAO
Bibliography
1.Manuel Filipe Santos e Carla Sousa Azevedo (2005) “Data Mining – Descoberta de Conhecimento em Bases de Dados”, FCA Editora de Informática.
2. Maribel Yasmina Santos e Isabel Ramos (2009) “Business Intelligence – Tecnologias da Informação na Gestão de Conhecimento – 2.ª Edição Actualizada e Aumentada”, FCA Editora de Informática.
3. Konstantinos Tsiptsis, Antonios Chorianopoulos (2010), “Data Mining Techniques in CRM: Inside Customer Segmentation”, Wiley-Blackwell.
4. Alex Berson, Kurt Thearling, Stephen J. Smith (2002), “Building Data Mining Applications for CRM”, McGraw-Hill.
5. Olivia Parr Rud (2000), “Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management”, John Wiley & Sons.
6. Michael J. Berry and Gordon S. Linoff (2000), “Mastering Data Mining: The Art and Science of Customer Relationship Management”, John Wiley & Sons.