Data Mining and Business Analytics with R
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- Hardcover: 368 pages
- Publisher: Wiley; 1 edition (May 28, 2013)
- Language: English
- ISBN-10: 111844714X
- ISBN-13: 978-1118447147
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.
Data Mining: Practical Machine Learning Tools and Techniques
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Ian H. Witten, Eibe Frank, Mark A. Hall
Publication Date: January 20, 2011 | ISBN-10: 0123748569 | ISBN-13: 978-0123748560 | Edition: 3
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Konstantinos Tsiptsis , Antonios Chorianopoulos
A complete and comprehensive handbook for the application of data mining techniques in marketing and customer relationship management. It combines a technical and a business perspective, bridging the gap between data mining and its use in marketing.
A Practical Guide to Using Business Rules and Predictive Analytics
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The book is aimed at those at the intersection of business and technology: executives who take an interest in technology and who use it to drive innovation and better business results, and technologists who want to use technology to transform the business of their organization.
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This book constitutes the refereed proceedings of the International Conference on Web Information Systems and Mining, WISM 2010, held in Sanya, China, on October 23-24, 2010. The 54 revised full papers presented in this volume were carefully reviewed and selected from 603 submissions.
Models and Algorithms (Advances in Database Systems)
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Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field.
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Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation
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Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data.
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Product Description: Due to the Internet Revolution, human conversational data — in written forms — are accumulating at a phenomenal rate. At the same time, improvements in speech technology enable many spoken conversations to be transcribed. Individuals and organizations engage in email exchanges, face-to-face meetings, blogging, texting and other social media activities.
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Min Song, Yi-Fang Brook Wu
The massive daily overflow of electronic data to information seekers creates the need for better ways to digest and organize this information to make it understandable and useful. Text mining, a variation of data mining, extracts desired information from large, unstructured text collections stored in electronic forms.