Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining PDF Download

Author: Inderjit S. Dhillon
Publisher:
ISBN: 9781450321747
Category : Computer science
Languages : en
Pages : 1534

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Author: Robert Grossman
Publisher:
ISBN: 9781450325721
Category :
Languages : en
Pages :

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KDD'13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Aug 11, 2013-Aug 14, 2013 Chicago, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

Author:
Publisher:
ISBN: 9781450362016
Category : Data mining
Languages : en
Pages :

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Author: Lingfei Wu
Publisher: Springer Nature
ISBN: 9811660549
Category : Computers
Languages : en
Pages : 701

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Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

Author: Petra Perner
Publisher: Springer
ISBN: 3319627015
Category : Computers
Languages : en
Pages : 346

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This book constitutes the refereed proceedings of the 17th Industrial Conference on Advances in Data Mining, ICDM 2017, held in New York, NY, USA, in July 2017. The 27 revised full papers presented were carefully reviewed and selected from 71 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine, and in process control in industry and society.

Author: Ron Kohavi
Publisher: Cambridge University Press
ISBN: 1108590098
Category : Computers
Languages : en
Pages : 291

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Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice.

Author: José L. Balcázar
Publisher: Springer Science & Business Media
ISBN: 364215882X
Category : Computers
Languages : en
Pages : 538

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This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2010, held in Barcelona, Spain, in September 2010. The 120 revised full papers presented in three volumes, together with 12 demos (out of 24 submitted demos), were carefully reviewed and selected from 658 paper submissions. In addition, 7 ML and 7 DM papers were distinguished by the program chairs on the basis of their exceptional scientific quality and high impact on the field. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. A topic widely explored from both ML and DM perspectives was graphs, with motivations ranging from molecular chemistry to social networks.

Author: Longbing Cao
Publisher:
ISBN: 9781450336642
Category : Computer science
Languages : en
Pages : 2338

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Author: Bettina Berendt
Publisher:
ISBN: 9783662185919
Category :
Languages : en
Pages : 218

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Author: Oded Maimon
Publisher: Springer Science & Business Media
ISBN: 038725465X
Category : Computers
Languages : en
Pages : 1378

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Book Description
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.