Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in todays world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.
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Reviewer: Robert M. Lynch
Hofmann and Klinkenberg have produced a fine collection of essays on data mining and analytic models, presented in several cross-disciplinary cases. This book describes data mining and case applications using Rapidminer models and analytic techniques (http://www.Rapidminer.com). Rapidminer is a system for the design and documentation of an overall data mining process. The system offers a comprehensive set of operators and structures that can be used to express the control flow of the process using drag-and-drop tools. The book focuses on the fine details of Rapidminer, from data preparation to model development to evaluating and visualizing the results; further support is freely offered at the website. The book represents the work of more than 30 contributors. Managing the writing styles of so many contributors is a challenging task, and the editors are to be commended for their effort. The material flows well, is very readable, and easily transitions from chapter to chapter and section to section. The book is divided into ten sections, each focusing on a different disciplinary area and a different analytic and mining model. Each section includes one or more cases. Section 1 introduces Rapidminer and data mining in general. Section 2 discusses basic classification, using cases in credit approval, teaching assistant selection, and nursery school selection or rejection. The classification models consider k -nearest neighbor classification and naîve Bayesian classification. Section 3 explores cases in marketing, cross-selling, and recommender systems for higher education programs. Section 4 focuses on medical and educational cases, with a focus on clustering algorithms. Section 5 discusses the mining of text rather than numerical variable data, such as in spam detection, language identification, and customer feedback. Rapidminer includes a text processing extension. Section 6 covers feature selection and classification in astroparticle physics and carpal tunnel syndrome in medicine. A feature selection extension is available in Rapidminer. Section 7 presents models and mining results for datasets in molecular structure and the modeling of property-activity relationships in biochemistry and medicine. Section 8 focuses on image mining, including feature extraction, segmentation, and classification. Section 9 examines anomaly detection, instance selection, and the construction of prototypes. Finally, Section 10 models meta-learning and automated learner selection. In each section, the authors introduce a mining activity, the related model, and the analytic techniques used. The datasets are described, the Rapidminer requirements are enumerated, and the analysis is summarized. The reader can refer to the companion web site for downloadable code and datasets for each case (http://www.rapidminerbook.com). If you would like more information about the book, this is the place to look. Data mining requires a basic knowledge of clustering and classification algorithms, linear models, principal component analysis, and factor analysis, among other grouping and discrimination techniques. A background in such topics would be helpful for reading the book. Similarly, some technical savvy will be useful. Tutorials, manuals, and other support material for Rapidminer are available at http://rapid-i.com. This is a good book. If you are interested in some very interesting data mining cases, or if you would like to learn Rapidminer, it will not disappoint. The bibliographic references are lengthy and the indices are well done. More reviews about this item: Amazon Online Computing Reviews Service
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Lots of data from different domains are published as Linked Open Data (LOD). While there are quite a few browsers for such data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web .
Today's organizations, including online businesses, use the art of data-driven decision-making i.e. business-intelligence (BI) to benefit from all the data out in the open. Given the current market demand for BI skill-sets, including the knowledge of .
FGCN '08: Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 03
Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I’s RapidMiner as our tool to analyze a Pima Indians Diabetes Data Set, which collects the information of patients with .
