"Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management" - это книга, наполненная более чем сорока процентами нового и обновленного материала. В этом издании бизнес-менеджеры, маркетинговые аналитики и специалисты по добыче данных узнают, как использовать основные методы и техники добычи данных для решения типичных бизнес-проблем. Каждая глава охватывает новый метод добычи данных, а затем показывает, как применять этот метод для улучшения маркетинга, продаж и поддержки клиентов. Авторы славятся своей лаконичностью, ясностью и практическими объяснениями сложных концепций, делая эту книгу идеальным введением в добычу данных. Более продвинутые главы охватывают такие темы, как подготовка данных к анализу и создание необходимой инфраструктуры для добычи данных. В книге рассматриваются основные методы добычи данных, включая деревья принятия решений, нейронные сети, коллаборативную фильтрацию, ассоциативные правила, анализ связей, кластерный анализ и анализ выживаемости.
This edition, packed with over 40 percent new and refreshed content, illustrates for business managers, marketers, data-mining experts, how fundamental mining approaches and procedures can assist in solving typical business issues. Each chapter features a newly explained mining procedure that clears up how to utilize it for better marketing, selling, and client care. The author builds on their achievement of succinct, clear, palpable explanations for complicated ideas, making the book an absolute beginning to data mining. Earthly chapters illuminate subjects, like how to put data into analysis and how build the required infrastructure for data hanging. This book comprehensively embraces fundamental data mineral processing techniques, encompassing prediction trees, neurological networks, joint filtering, influence rules, connect analysis, classification, and life analysis. After many years you still don't know anyone who knows anything about Data Mining... but you're sure to become one after looking at this clear, instinctive way of looking at mining techniques.
More than 40 percent of the content in this revised edition is new or has been previously unpublished. This book provides managers of businesses, market analysts and data miners with guidance on how to leverage fundamental data mining procedures and methods to address common business problems. Each chapter outlines a unique data mining process and shows how it can be leveraged to improve marketing activities, sales efforts and customer service, building on the authors' track record of providing definitive, down-to-earth explanations that simplify complex subject areas with their deliverables. Updated chapters provide insights on preparing data sources for analytical integration and developing the necessary tools to facilitate data mining. It also presents essential data mining techniques including decision-making trees, neural net work systems, collaborative recommendation engines, association rule mining, link-analysis, cluster analysis, and time-series modeling.
Электронная Книга «Data Mining Techniques. For Marketing, Sales, and Customer Relationship Management» написана автором Gordon Linoff S. в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9780764569074
Описание книги от Gordon Linoff S.
Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis
