Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy.

Big Data Mining for Climate Change

Business & Money

Li, Jianping

Zhang, Zhihua

Economics

Elsevier

Optimization Based Data Mining: Theory and Applications (Advanced Information and Knowledge Processing)

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.
Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.
Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

Computers & Technology

Tian, Yingjie

Shi, Yong

Kou, Gang

Peng, Yi

Computer Science

Springer

Optimization techniques have been widely adopted to implement various data mining algorithms. This book focuses on cutting-edge theoretical developments and real-life applications in optimization, covering a range of fields from finance to bioinformatics.

Financial Statements-Based Bank Risk Aggregation (Innovation in Risk Analysis)

Wei, Lu

Zhu, Xiaoqian

Reference

Risk Matrix: Rating Scheme Design and Risk Aggregation (Innovation in Risk Analysis)

Bao, Chunbing

Wu, Dengsheng

This book focuses on discussing the issues of rating scheme design and risk aggregation of risk matrix, which is a popular risk assessment tool in many fields. Although risk matrix is usually treated as qualitative tool, this book conducts the analysis from the quantitative perspective. The discussed content belongs to the scope of risk management, and to be more specific, it is related to quick risk assessment. This book is suitable for the researchers and practitioners related to qualitative or quick risk assessment and highly helps readers understanding how to design more convincing risk assessment tools and do more accurate risk assessment in a uncertain context.

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Books by Li, Jianping