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This volume contains research on how we measure poverty, inequality and welfare and how we use such measurements to devise policies to deliver social mobility. It contains ten papers, some of which were presented at the third meeting of The Theory and Empirics of Poverty, Inequality and Mobility at Queen Mary University of London, London, October 2016. The volume begins with theoretical issues at the frontier of the literature. Three papers discuss the impact of social welfare policies on poverty measurement, and with innovations on the measurement of relative bipolarisation. Two papers address the conceptualisation of multidimensional poverty by incorporating inequality within the poor, and that of chronic poverty for time dependent analyses, with applications to India and Haiti, and Ethiopia respectively. The second half of the volume consists of empirical contributions, using novel techniques and datasets to investigate the dynamics of poverty and welfare. These studies track the dynamics of poverty using unique datasets for China, the Caucasus and Italy. The volume concludes with investigations about within-household inequalities between siblings due to the unequal effects of conditional cash transfers in Cambodia and a cross-country study on the effect of historical income inequality on entrepreneurship in developing countries.

Income distribution. --- Poverty. --- Welfare economics. --- Economic policy --- Economics --- Social policy --- Destitution --- Wealth --- Basic needs --- Begging --- Poor --- Subsistence economy --- Distribution of income --- Income inequality --- Inequality of income --- Distribution (Economic theory) --- Disposable income --- Equality --- Income distribution --- Poverty --- Well-being --- Welfare (Personal well-being) --- Wellbeing --- Quality of life --- Happiness --- Health --- Egalitarianism --- Inequality --- Social equality --- Social inequality --- Political science --- Sociology --- Democracy --- Liberty --- Economic aspects --- E-books --- Economic aspects. --- Equality Economic aspects

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Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.

Information Technology --- Artificial Intelligence --- Cluster analysis -- Data processing. --- Cluster analysis. --- Multivariate analysis. --- Structural equation modeling. --- Pattern perception --- Cluster analysis --- Engineering & Applied Sciences --- Mechanical Engineering --- Computer Science --- Mechanical Engineering - General --- Data processing --- Pattern perception. --- Data processing. --- Design perception --- Pattern recognition --- Computer science. --- Computers. --- Artificial intelligence. --- Bioinformatics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Computational Biology/Bioinformatics. --- Information Systems and Communication Service. --- Form perception --- Perception --- Figure-ground perception --- Information systems. --- Artificial Intelligence. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace

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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.

Pattern perception. --- Machine learning. --- Genetic algorithms. --- Automatic classification. --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Learning, Machine --- Artificial intelligence --- Machine theory --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Automatic indexing --- Classification --- Information storage and retrieval systems --- Optical pattern recognition. --- Computer science. --- Telecommunication. --- Artificial intelligence. --- Bioinformatics. --- Pattern Recognition. --- Programming Techniques. --- Communications Engineering, Networks. --- Artificial Intelligence. --- Complex Systems. --- Computational Biology/Bioinformatics. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Electric communication --- Mass communication --- Telecom --- Telecommunication industry --- Telecommunications --- Communication --- Information theory --- Telecommuting --- Informatics --- Science --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data processing --- Pattern recognition. --- Computer programming. --- Electrical engineering. --- Statistical physics. --- Dynamical systems. --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mathematical statistics --- Electric engineering --- Engineering --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Statistical methods --- Programming --- Pattern recognition systems. --- System theory. --- Automated Pattern Recognition. --- Computational and Systems Biology. --- Systems, Theory of --- Systems science --- Pattern classification systems --- Pattern recognition computers --- Computer vision --- Philosophy

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This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

Genetic algorithms --- Cluster analysis --- Multiple criteria decision making --- Data mining --- Bioinformatics --- Engineering & Applied Sciences --- Mechanical Engineering --- Civil & Environmental Engineering --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Operations Research --- Computer Science --- Mechanical Engineering - General --- Mathematical models --- Information Technology --- Artificial Intelligence --- Genetic algorithms. --- Multiple criteria decision making. --- Data mining. --- Bioinformatics. --- Bio-informatics --- Biological informatics --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Decision making with multiple objectives --- MCDM (Decision making) --- Multiattribute decisions --- Multicriteria decision analysis --- Multicriteria decision making --- Multicriteria decision making analysis --- Multiobjective decision making --- Multiple objective decision making --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Computer science. --- Artificial intelligence. --- Computational intelligence. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Computational Biology/Bioinformatics. --- Data Mining and Knowledge Discovery. --- Computational Intelligence. --- Biology --- Information science --- Computational biology --- Systems biology --- Database searching --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Data processing --- Decision making --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Engineering. --- Artificial Intelligence. --- Construction --- Industrial arts --- Technology --- Mathematical models. --- Mathematics.