Data mining process models: a roadmap for knowledge discovery

• Author(s): Mendes, Armando B. ; Cavique, Luís ; Santos, Jorge M. A.
• Date: 2012
• Persistent ID: http://hdl.handle.net/10400.2/2794
• Origin: Repositório Aberto da Universidade Aberta
• Subject(s): Data mining; knowledge discovery
• Document Type: Book part
• Language: English
• Contributor(s): Repositório Aberto

Description

Extracting knowledge from data is the major objective of any data analysis process, including the ones developed in several sciences as statistics and quantitative methods, data base \ data warehouse and data mining. From the latter disciplines the data mining is the most ambitious because intends to analyse and extract knowledge from massive often badly structured data with many specific objectives. It is also used for relational data base data, network data, text data, log file data, and data in many other forms. In this way, is no surprise that a myriad of applications and methodologies have been and are being developed and applied for data analysis functions, where CRISP-DM (cross industry standard process for data mining) and SEMMA (sample, explore, modify, model, assessment) are two examples. The need for a roadmap is, therefore, highly recognised in the field and almost every software company has established their own process model.