Angielski
Data mining is used today in many different fields including banking, financial analysis of markets, insurance and private health sectors, education, industrial processes, medicine, biology and bioengineering, telecommunication. But regardless of the field in which it is applied, the core concepts and tasks of data mining do not require nor domain-specific knowledge, nor advanced mathematical treatments. Data Mining Techniques with SAS Enterprise Miner presents the most common techniques used in SAS data mining in a simple and easy to understand way using SAS Enterprise Miner, regardless of the specific field you're working in, and without needing to draw on complicated mathematical algorithms.§§Data Mining Techniques using SAS Enterprise Miner therefore describes data mining techniques to you in accessible language and clear, practical, hands-on examples and exercises. Each chapter presents a data mining case study that's presented to you completely, including the results of the case study you've built and an interpretation of its results, which is so vital of course to your data mining work.§§ Data Mining Techniques using SAS Enterprise Miner begins with an introduction to data mining data and its distinct phases. You'll then learn how to develop the initial phases which include the selection of information, data exploration, data cleansing, transformation of data, and related issues. After these initial data mining phases, this book goes into practical, hands-on detail on both predictive and descriptive data mining techniques. The predictive techniques you'll learn about cover all models of regression, discriminant analysis, decision trees, neural networks, and other model-based techniques. The descriptive techniques then work with variable dimension reduction techniques, techniques of classification and segmentation (clustering), and exploratory data analysis techniques.§§
