Data Mining Techniques Using MATLAB (Paperback) | Released: 16 Dec 2018

Data mining is the process of discovering actionable information from large sets of data. Data mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or because there is too much data. These patterns and trends can be collected and defined as a data mining model. Mining models can be applied to specific scenarios, such as: - Forecasting: Estimating sales, predicting server loads or server downtime - Risk and probability: Choosing the best customers for targeted mailings, determining the probable break-even point for risk scenarios, assigning probabilities to diagnoses or other outcomes - Recommendations: Determining which products are likely to be sold together, generating recommendations - Finding sequences: Analyzing customer selections in a shopping cart, predicting next likely events - Grouping: Separating customers or events into cluster of related items, analyzing and predicting affinities Building a mining model is part of a larger process that includes everything from asking questions about the data and creating a model to answer those questions, to deploying the model into a working environment. 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. All chapters are supplemented by examples that clarify the techniques.