Present the terminology and general concepts related to the domain of knowledge extraction in database systems, with particular emphasis on data warehousing systems.
Know how to justify the implementation of a knowledge extraction system in an organization as a fundamental element for a decision support system.
Know and apply the knowledge extraction life cycle within an organization.
Know and apply, according to their characteristics, the different techniques and models of knowledge extraction and data mining in concrete application cases.
Know how to justify, design and implement a knowledge extraction system using a data warehousing system as data source.
Program
Introduction to knowledge extraction systems.
Knowledge discovery life cycle.
Introduction to machine learning and data mining.
Data preparation and pre-processing.
Languages and architectures for data mining in data warehouses.
Data classification and prediction techniques and models.
Mining association rules and sequences.
Generation and analysis of clusters.
Mining complex data types.
Bibliography
Golfarelli, M., Rizzi, S., Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Osborne Media; 1st Edition, May 26, 2009.
Bhatia, P., Data Mining and Data Warehousing: Principles and Practical Techniques, 1st Edition Cambridge University Press; 1st edition, June 27, 2019.
Linoff, G., Berry, M., Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd Edition, Wiley, April 12, 2011. ISBN-10 : 0470650931, ISBN-13 : 978-0470650936.
Witten, I., Frank, E., Hall, M., Pal, C., Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) 4th Edition, Morgan Kaufmann, December 1, 2016. ISBN-10 : 0128042915, ISBN-13 : 978-0128042915.