Discovering Knowledge in Data - An Introduction to Data Mining 2e, Larose
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Leskovec Jure Название: Mining of Massive Datasets ISBN: 1108476341 ISBN-13(EAN): 9781108476348 Издательство: Cambridge Academ Рейтинг: Цена: 10771.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Автор: Tao Li; Mitsunori Ogihara; George Tzanetakis Название: Music Data Mining ISBN: 1439835527 ISBN-13(EAN): 9781439835524 Издательство: Taylor&Francis Рейтинг: Цена: 16843.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing.
The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining.
The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.
Автор: Charu C. Aggarwal Название: Machine Learning for Text ISBN: 3030088073 ISBN-13(EAN): 9783030088071 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.
Автор: Ashok N. Srivastava, Mehran Sahami Название: Text Mining ISBN: 1420059408 ISBN-13(EAN): 9781420059403 Издательство: Taylor&Francis Рейтинг: Цена: 15004.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Focuses on statistical methods for text mining and analysis. This work examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search.
Автор: S. Sumathi; S.N. Sivanandam Название: Introduction to Data Mining and its Applications ISBN: 3662500809 ISBN-13(EAN): 9783662500804 Издательство: Springer Рейтинг: Цена: 41787.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining.
Автор: Yang, Xin-She Название: Yang - INTRODUCTION TO ALGORITHMS FOR DATA MINING AND MAC... ISBN: 0128172169 ISBN-13(EAN): 9780128172162 Издательство: Elsevier Science Рейтинг: Цена: 9936.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning as well as optimization. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modelling skills so they can process and interpret data for classification, clustering, curve-fitting, and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics
Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study
Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
Автор: Pang-Ning Tan, Michael Steinbach, Vipin Kumar Название: Introduction to Data Mining, 2 ed. ISBN: 0133128903 ISBN-13(EAN): 9780133128901 Издательство: Pearson Education Рейтинг: Цена: 37854.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introducing the fundamental concepts and algorithms of data mining
Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.
Автор: Leetaru Kalev Название: Data Mining Methods for the Content Analyst ISBN: 0415895146 ISBN-13(EAN): 9780415895149 Издательство: Taylor&Francis Рейтинг: Цена: 6583.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike. Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.
Автор: Dan A. Simovici; Chabane Djeraba Название: Mathematical Tools for Data Mining ISBN: 1447164067 ISBN-13(EAN): 9781447164067 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc.
Описание: Shows how innovation in computer vision methods can markedly lower the costs of using images as data. Introduces readers to deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. Provides guidance and instruction for scholars interested in using these methods in their own research.
Автор: Zafarani Название: Social Media Mining ISBN: 1107018854 ISBN-13(EAN): 9781107018853 Издательство: Cambridge Academ Рейтинг: Цена: 9027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Social Media Mining integrates social media, social network analysis, and data mining to provide a coherent platform for students, practitioners, researchers and project managers to understand the basics and potentials of social media mining. It presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
