Managing And Mining Graph Data

Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 1441960457
Size: 61.22 MB
Format: PDF
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Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.

Managing And Mining Uncertain Data

Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 0387096906
Size: 49.53 MB
Format: PDF, ePub
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Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.

Data Streams

Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 0387475346
Size: 20.58 MB
Format: PDF, Kindle
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Discusses issues related to the mining aspects of data streams. Each chapter in this book contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. It is intended for a professional audience composed of researchers and practitioners in industry.

Nonlinear Dimensionality Reduction

Author: John A. Lee
Publisher: Springer Science & Business Media
ISBN: 038739351X
Size: 73.83 MB
Format: PDF
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This book describes established and advanced methods for reducing the dimensionality of numerical databases. Each description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. The text provides a lucid summary of facts and concepts relating to well-known methods as well as recent developments in nonlinear dimensionality reduction. Methods are all described from a unifying point of view, which helps to highlight their respective strengths and shortcomings. The presentation will appeal to statisticians, computer scientists and data analysts, and other practitioners having a basic background in statistics or computational learning.

Privacy Preserving Data Mining

Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 0387709924
Size: 26.57 MB
Format: PDF, Docs
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Advances in hardware technology have increased the capability to store and record personal data. This has caused concerns that personal data may be abused. This book proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions of a particular topic in privacy. The book is designed for researchers, professors, and advanced-level students in computer science, but is also suitable for practitioners in industry.

Data Mining Concepts And Techniques

Author: Jiawei Han
Publisher: Elsevier
ISBN: 9780123814807
Size: 14.73 MB
Format: PDF
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Microsoft Data Mining

Author: Barry de Ville
Publisher: Elsevier
ISBN: 0080491847
Size: 41.40 MB
Format: PDF
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Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes. Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management. Unique book on a hot data management topic Part of Digital Press's SQL Server and data mining clusters Author is an expert on both traditional and Microsoft data mining technologies

Advances In Databases And Information Systems

Author: Tadeusz Morzy
Publisher: Springer Science & Business Media
ISBN: 3642327419
Size: 11.46 MB
Format: PDF
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This volume is the second one of the 16th East-European Conference on Advances in Databases and Information Systems (ADBIS 2012), held on September 18-21, 2012, in Poznań, Poland. The first one has been published in the LNCS series. This volume includes 27 research contributions, selected out of 90. The contributions cover a wide spectrum of topics in the database and information systems field, including: database foundation and theory, data modeling and database design, business process modeling, query optimization in relational and object databases, materialized view selection algorithms, index data structures, distributed systems, system and data integration, semi-structured data and databases, semantic data management, information retrieval, data mining techniques, data stream processing, trust and reputation in the Internet, and social networks. Thus, the content of this volume covers the research areas from fundamentals of databases, through still hot topic research problems (e.g., data mining, XML data processing), to novel research areas (e.g., social networks, trust and reputation, and data stream processing). The editors of this volume believe that its content will inspire the researchers with new ideas for future development. It may also serve as an overview of the ongoing work in the field of databases and information systems.

Advancing Big Data Benchmarks

Author: Tilmann Rabl
Publisher: Springer
ISBN: 3319105965
Size: 44.55 MB
Format: PDF
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This book constitutes the thoroughly refereed joint proceedings of the Third and Fourth Workshop on Big Data Benchmarking. The third WBDB was held in Xi'an, China, in July 2013 and the Fourth WBDB was held in San José, CA, USA, in October, 2013. The 15 papers presented in this book were carefully reviewed and selected from 33 presentations. They focus on big data benchmarks; applications and scenarios; tools, systems and surveys.

Mining Of Massive Datasets

Author: Jure Leskovec
Publisher: Cambridge University Press
ISBN: 1107077230
Size: 20.76 MB
Format: PDF, Docs
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Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.