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Analysis and Modeling of Complex Data in Behavioral and Social Sciences
This volume presents theoretical developments, applications and computational methods for the analysis and modeling in behavioral and social sciences where data are usually complex to explore and investigate. The challenging proposals provide a connection between statistical methodology and the social domain with particular attention to computational issues in order to effectively address complicated data analysis problems. The papers in this volume stem from contributions initially presented at the joint international meeting JCS-CLADAG held in Anacapri (Italy) where the Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society had a stimulating scientific discussion and exchange.
Operations Research Proceedings 2010
This book contains selected papers from the symposium "Operations Research 2010" which was held from September 1-3, 2010 at the "Universität der Bundeswehr München", Germany. The international conference, which also serves as the annual meeting of the German Operations Research Society (GOR), attracted more than 600 participants from more than thirty countries. The general theme "Mastering Complexity" focusses on a natural component of the globalization process. Financial markets, traffic systems, network topologies and, last but not least, energy resource management, all contain complex behaviour and economic interdependencies which necessitate a scientific solution. Operations Research is one of the key instruments to model, simulate and analyze such systems. In the process of developing optimal solutions, suitable heuristics and efficient procedures are some of the challenges which are discussed in this volume.
Classification, (Big) Data Analysis and Statistical Learning
This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8–10, 2015.
Data Mining for Biomarker Discovery
Biomarker discovery is an important area of biomedical research that may lead to significant breakthroughs in disease analysis and targeted therapy. Biomarkers are biological entities whose alterations are measurable and are characteristic of a particular biological condition. Discovering, managing, and interpreting knowledge of new biomarkers are challenging and attractive problems in the emerging field of biomedical informatics. This volume is a collection of state-of-the-art research into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques. This volume is intended for students, and researchers in bioinformatics, proteomics, and genomics, as well engineers and applied scientists interested in the interdisciplinary application of data mining techniques.
Computational Optimization
Computational Optimization: A Tribute to Olvi Mangasarian serves as an excellent reference, providing insight into some of the most challenging research issues in the field. This collection of papers covers a wide spectrum of computational optimization topics, representing a blend of familiar nonlinear programming topics and such novel paradigms as semidefinite programming and complementarity-constrained nonlinear programs. Many new results are presented in these papers which are bound to inspire further research and generate new avenues for applications. An informal categorization of the papers includes: Algorithmic advances for special classes of constrained optimization problems Analysis of linear and nonlinear programs Algorithmic advances B- stationary points of mathematical programs with equilibrium constraints Applications of optimization Some mathematical topics Systems of nonlinear equations.
Exploiting Semantic Web Knowledge Graphs in Data Mining
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation st...
High Performance Algorithms and Software in Nonlinear Optimization
This book contains a selection of papers presented at the conference on High Performance Software for Nonlinear Optimization (HPSNO97) which was held in Ischia, Italy, in June, 1997. The book provides an overview of the nonlinear optimization field, including algorithms, software evaluation, implementation issues, applications, and areas of research, through authoritative papers by some of the most active and well-known researchers in the field. The papers of the Proceedings can be recommended to mathematicians, physicists, and engineers working in the fields mentioned above, as well as recommended for further reading within graduate studies.
Analysis of Multi-megabyte Secondary CPU Cache Memories
Since the placement of pages in main memory also places data in the cache, a poor page placement will cause poor cache performance. This dissertation introduces several new careful page mapping algorithms to improve the page placement, and shows that they eliminate 10%-20% of the direct-mapped real-indexed cache misses for the long traces. In other words, this dissertation develops software techniques that can make a hardware direct-mapped cache appear about 50% larger."
