Data Mining Techniques With Mastering Data Mining Set

Data Mining Techniques with Mastering Data Mining Set

MASTERING DATA MINING: THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT

Special Features: · Best-in-class data mining techniques for solving critical problems in all areas of business· Explains how to pick the right data mining techniques for specific problems· Shows how to perform analysis and evaluate results· Features real-world examples from across various industry sectors· Companion Web site with updates on data mining products and service providers About The Book: Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with mining or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of database marketing managers.

Mastering Data Mining Techniques

The illustrations in this book are created by “Team Educohack”. Mastering Data Mining Techniques is your comprehensive guide to extracting valuable insights from corporate databases. This book demonstrates how data mining has evolved into an essential tool for modern business, with updates and revisions to all chapters, plus new additions. We provide clear explanations of complex topics using concise language, minimizing jargon and formulas. Technical subjects are illustrated with real-world examples and case studies, offering practical tips for marketing analysts, business managers, and data mining professionals. We cover linear and logistic regression, clustering methods, and an overview of data mining applications, establishing a business context and methodologies common to all projects. Data mining is a crucial step in the KDD process, used for conceptual explanations, related analysis, model construction, data clustering, and time-series trend modeling. We emphasize the importance of measures of interest, detailing their relevance and guiding the data mining process. The book also explores data warehousing and multidimensional databases as interlayers between data sources, allowing integration of online analytical processing and data mining. Starting with an overview of data warehousing concepts, we propose an integrated OLAM architecture.