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Data Mining: Concepts and Techniques

Формат:      Страниц 770
     твердый переплет
Jiawei H.    
Elsevier Urban & Partner    
4122 руб
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Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. Whether you are a seasoned professional or a new student of data mining, this book has much to offer you: * A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data. * Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning. * Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data — including stream data, sequence data, graph structured data, social network data, and multi-relational data. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. Our ability to generate and collect data has been increasing rapidly.
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Data Mining, Visual Mining, Text Mining, OLAP. Гриф УМО ВУЗов России (+ CD-ROM) Технологии анализа данных: Учебное пособие БХВ-Петербург Барсегян А.А.
Книга является вторым, обновленным и дополненным, изданием учебного пособия "Методы и модели анализа данных: OLAP и Data Mining".
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Chapter 2 introduces background work on data mining, share mining, utility mining, and privacy-preserving data mining.
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Based on the conceptual framework, a prototype of Interactive Classification System (ICS) has been implemented.
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Almost every statistics instructor emphasizes to their classes that a strong association does not necessarily imply causation.
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More specifically we address the task of gene name disambiguation.
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While it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value . . . of a user-specified goal attribute based on the values of other attributes. " For instance, a banking institution might want to predict whether a customer' s credit would be "good" or "bad" based on their age, income and current savings. Evolutionary algorithms for data mining work by creating a series of random rules to be checked against a training dataset. The rules which most closely fit the data are selected and are mutated. The process is iterated many times and eventually, a rule will arise that approaches 100% similarity with the training data. This rule is then checked against a test dataset, which was previously invisible to the genetic algorithm. Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary algorithms. High Quality Content by WIKIPEDIA articles!
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The findings of the Monte Carlo simulations to address the first main interest showed that the results yielded by the iteration algorithm are comparable to those produced by a well-known statistical package.
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Молочный гриб можно использовать для похудения, восстановления микрофлоры, очищения организмаМолочный гриб можно использовать для похудения, восстановления микрофлоры, очищения организма

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