Data Mining by Examples Using MATLAB

by C. Perez

The availability of large volumes of data and the use of computer tools has transformed the research and analysis of data orienting it towards certain specialized techniques included under the name of Data Mining. Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining and processing large amounts of data organized according to Big Data techniques. Data Mining methodologies include SAS Institute's SEMMA methodology and IBM's CRISP-DM methodology. -SAS Institute defines the concept of Data Mining as the process of Selecting, Exploring, Modifying, Modeling and Assessment large amounts of data with the aim of uncovering unknown knowledge in databases. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute. -IBM provides a complete methodology for ordering data mining tasks. The foundation is similar to SAS. CRISP-DM considers the process of extraction of knowledge from the data through 6 phases: Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Model deployment. MATLAB has tools to work in the different phases of Data Mining. In this book are developed several chapters that include phases of Data Mining. The chapter Data Processing includes Selection and Modification phases. The chapter Data Exploration includes the Exploring phase. Tthe chapters on Predictive Techniques include the Modlization phase. The chapter on Classification Techniques include the Modeling and Modification phases. All chapters are supplemented by examples that clarify the techniques.

Paperback

Published October 22, 2017