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This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness. .
Computer science. --- Algorithms. --- Data mining. --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Mining and Knowledge Discovery. --- Algorithm Analysis and Problem Complexity. --- Pattern recognition systems. --- Pattern classification systems --- Pattern recognition computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Pattern perception --- Computer vision --- Optical pattern recognition. --- Computer software. --- Software, Computer --- Computer systems --- Optical data processing --- Perceptrons --- Visual discrimination --- Algorism --- Algebra --- Arithmetic --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Foundations
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This book provides a general and comprehensible overview of supervised descriptive pattern mining, considering classic algorithms and those based on heuristics. It provides some formal definitions and a general idea about patterns, pattern mining, the usefulness of patterns in the knowledge discovery process, as well as a brief summary on the tasks related to supervised descriptive pattern mining. It also includes a detailed description on the tasks usually grouped under the term supervised descriptive pattern mining: subgroups discovery, contrast sets and emerging patterns. Additionally, this book includes two tasks, class association rules and exceptional models, that are also considered within this field. A major feature of this book is that it provides a general overview (formal definitions and algorithms) of all the tasks included under the term supervised descriptive pattern mining. It considers the analysis of different algorithms either based on heuristics or based on exhaustive search methodologies for any of these tasks. This book also illustrates how important these techniques are in different fields, a set of real-world applications are described. Last but not least, some related tasks are also considered and analyzed. The final aim of this book is to provide a general review of the supervised descriptive pattern mining field, describing its tasks, its algorithms, its applications, and related tasks (those that share some common features). This book targets developers, engineers and computer scientists aiming to apply classic and heuristic-based algorithms to solve different kinds of pattern mining problems and apply them to real issues. Students and researchers working in this field, can use this comprehensive book (which includes its methods and tools) as a secondary textbook.
Pattern recognition systems. --- Data mining. --- Artificial intelligence. --- Optical pattern recognition. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence. --- Pattern Recognition. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- 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 --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception
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After the Spanish victories over the Inca claimed Tawantinsuyu for Charles V in the 1530s, native Andeans undertook a series of perilous trips from Peru to the royal court in Spain. Ranging from an indigenous commoner entrusted with delivering birds of prey for courtly entertainment to an Inca prince who spent his days amid titles, pensions, and other royal favors, these sojourners were both exceptional and paradigmatic. Together, they shared a conviction that the sovereign’s absolute authority would guarantee that justice would be done and service would receive its due reward. As they negotiated their claims with imperial officials, Amerindian peoples helped forge the connections that sustained the expanding Habsburg realm’s imaginary and gave the modern global age its defining character. Andean Cosmopolitans recovers these travelers’ dramatic experiences, while simultaneously highlighting their profound influences on the making and remaking of the colonial world. While Spain’s American possessions became Spanish in many ways, the Andean travelers (in their cosmopolitan lives and journeys) also helped to shape Spain in the image and likeness of Peru. De la Puente brings remarkable insights to a narrative showing how previously unknown peoples and ideas created new power structures and institutions, as well as novel ways of being urban, Indian, elite, and subject. As indigenous people articulated and defended their own views regarding the legal and political character of the “Republic of the Indians,” they became state-builders of a special kind, cocreating the colonial order.
Indians of South America --- Government relations --- Ethnic identity. --- Habsburg, House of. --- Peru --- Peru (Viceroyalty) --- History
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As technology advances, high volumes of valuable data are generated day by day in modern organizations. The management of such huge volumes of data has become a priority in these organizations, requiring new techniques for data management and data analysis in Big Data environments. These environments encompass many different fields including medicine, education data, and recommender systems. The aim of this book is to provide the reader with a variety of fields and systems where the analysis and management of Big Data are essential. This book describes the importance of the Big Data era and how existing information systems are required to be adapted to face up the problems derived from the management of massive datasets.
Computer science. --- Big data. --- Data sets, Large --- Large data sets --- Data sets --- Informatics --- Science --- Physical Sciences --- Engineering and Technology --- Information and Knowledge Engineering --- Computer and Information Science --- Data Management System
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he solution to many real-world problems lies in optimizing processes, parameters, or techniques, which requires dealing with immense search spaces. As such, finding solutions involves exhaustive methods to evaluate all possible solutions in the search for a global optimum. Some of these methods include evolutionary algorithms and genetic algorithms, both of which have proven to effectively deal with complex search spaces. This book focuses on genetic algorithms and their applications in various fields, including engineering and architecture.
Artificial intelligence --- Artificial intelligence. --- Philosophy.
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