The positive reciprocal pairwise comparison matrix (PCM) is one of the key components which is used to quantify the qualitative and/or intangible attributes into measurable quantities. This book examines six understudied issues of PCM, i.e. consistency test, inconsistent data identification and adjustment, data collection, missing or uncertain data estimation, and sensitivity analysis of rank reversal. The maximum eigenvalue threshold method is proposed as the new consistency index for the AHP/ANP. An induced bias matrix model (IBMM) is proposed to identify and adjust the inconsistent data, and estimate the missing or uncertain data. Two applications of IBMM including risk assessment and decision analysis, task scheduling and resource allocation in cloud computing environment, are introduced to illustrate the proposed IBMM.

This book constitutes the refereed proceedings of the 9th International Conference on Active Media Technology, AMT 2013, held in Maebashi, Japan, in October 2013. The 26 revised full papers presented together with 2 short papers, 16 workshop papers, and 12 special session papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on active computer systems, interactive systems, and application of AMT based systems; active media machine learning and data mining techniques; AMT for semantic web, social networks, and cognitive foundations. Additionally, the main topic of the workshop papers is: intelligence for strategic foresight; and for the special session papers: technologies and theories of narrative; evolutionary computation and its application; and intelligent media search techniques.

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.

The positive reciprocal pairwise comparison matrix (PCM) is one of the key components which is used to quantify the qualitative and/or intangible attributes into measurable quantities. This book examines six understudied issues of PCM, i.e. consistency test, inconsistent data identification and adjustment, data collection, missing or uncertain data estimation, and sensitivity analysis of rank reversal. The maximum eigenvalue threshold method is proposed as the new consistency index for the AHP/ANP. An induced bias matrix model (IBMM) is proposed to identify and adjust the inconsistent data, and estimate the missing or uncertain data. Two applications of IBMM including risk assessment and decision analysis, task scheduling and resource allocation in cloud computing environment, are introduced to illustrate the proposed IBMM.

This book provides cutting-edge research results and application experiencesfrom researchers and practitioners in multiple criteria decision making areas. It consists of three parts: MCDM Foundation and Theory, MCDM Methodology, and MCDM Applications. In Part I, it covers the historical MCDM development, the influence of MCDM on technology, society and policy, Pareto optimization, and analytical hierarchy process. In Part II, the book presents different MCDM algorithms based on techniques of robust estimating, evolutionary multiobjective optimization, Choquet integrals, and genetic search. In Part III, this book demonstrates a variety of MCDM applications, including project management, financial investment, credit risk analysis, railway transportation, online advertising, transport infrastructure, environmental pollution, chemical industry, and regional economy. The 17 papers of the book have been selected out of the 121 accepted papers at the 20th International Conference on Multiple Criteria Decision Making "New State of MCDM in 21st Century," held at Chengdu, China, in 2009. The 35 contributors of these papers stem from 10 countries."

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.