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Data Analysis for Complex Systems : A Linear Algebra Approach.
・ISBN 978-0-691-13918-0 paper US$ 35.00
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| 著者・編者 | Leibon, Greg / Pauls, Scott / Rockmore, Dan, |
|---|---|
| シリーズ | Primers in Complex Systems |
| 出版社 | (Princeton University Press, US) |
| 出版年月 | 2040 |
| ページ数 | 168 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-98＞ |
解説
The analysis of complex systems-from financial markets and voting patterns to ecosystems and food webs-can be daunting for newcomers to the subject, in part because existing methods often require expertise across multiple disciplines. This book shows how a single technique-the partition decoupling method-can serve as a useful first step for modeling and analyzing complex systems data. Accessible to a broad range of backgrounds and widely applicable to complex systems represented as high-dimensional or network data, this powerful methodology draws on core concepts in network modeling and analysis, cluster analysis, and a range of techniques for dimension reduction. The book explains these and other essential concepts and provides several real-world examples to illustrate how a data-driven approach can illuminate complex systems.
- Provides a comprehensive introduction to modeling and analysis of complex systems with minimal mathematical prerequisites
- Focuses on a single technique, thereby providing an easy entry point to the subject
- Explains analytic techniques using actual data from the social sciences
- Uses only linear algebra to model and analyze large data sets
- Includes problems and real-world examples
- An ideal textbook for students and invaluable resource for researchers with a wide range of backgrounds and preparation
- Proven in the classroom