RapidMiner

Data Mining Use Cases and Business Analytics Applications

Author: Markus Hofmann,Ralf Klinkenberg

Publisher: CRC Press

ISBN: 1498759866

Category: Business & Economics

Page: 525

View: 4489

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Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in today's 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 incre
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Data Preprocessing in Data Mining

Author: Salvador García,Julián Luengo,Francisco Herrera

Publisher: Springer

ISBN: 3319102478

Category: Computers

Page: 320

View: 3542

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Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.
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Feature Engineering for Machine Learning and Data Analytics

Author: Guozhu Dong,Huan Liu

Publisher: CRC Press

ISBN: 1351721275

Category: Business & Economics

Page: 400

View: 4693

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Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.
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Worterbuch Design

Author: Michael Erlhoff

Publisher: Birkhauser

ISBN: 9783764381431

Category: Design

Page: 472

View: 1433

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This dictionary provides a stimulating and categorical foundation for a serious international discourse on design. It is a handbook for everyone concerned with design in career or education, who is interested in it, enjoys it, and wishes to understand it. 110 authors from Japan, Austria, England, Germany, Australia, Switzerland, the Netherlands, the United States, and elsewhere have written original articles for this design dictionary. Their cultural differences provide perspectives for a shared understanding of central design categories and communicating about design. The volume includes both the terms in use in current discussions, some of which are still relatively new, as well as classics of design discourse. A practical book, both scholarly and ideal for browsing and reading at leisure.
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