Frequent Pattern Mining


Author: Charu C. Aggarwal,Jiawei Han
Publisher: Springer
ISBN: 3319078216
Category: Computers
Page: 471
View: 3274

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This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.

Frequent Pattern Mining


Author: Charu C. Aggarwal,Jiawei Han
Publisher: Springer
ISBN: 9783319346892
Category: Computers
Page: 471
View: 2522

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This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.

Advances in Knowledge Discovery and Data Mining

17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings
Author: Jian Pei,Vincent S. Tseng,Longbing Cao,Hiroshi Motoda,Guandong Xu
Publisher: Springer
ISBN: 3642374530
Category: Computers
Page: 610
View: 1044

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The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, applications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving data mining, recommendation, multimedia data mining, stream data mining, data preprocessing and representation.

Database Systems for Advanced Applications

14th International Conference, DASFAA 2009, Brisbane, Australia, April 21-23, 2009, Proceedings
Author: Xiaofang Zhou,Haruo Yokota,Ke Deng
Publisher: Springer Science & Business Media
ISBN: 3642008860
Category: Computers
Page: 797
View: 384

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This book constitutes the refereed proceedings of the 14th International Conference on Database Systems for Advanced Applications, DASFAA 2009, held in Brisbane, Australia, in April 2009. The 39 revised full papers and 22 revised short papers presented together with 3 invited keynote papers, 9 demonstration papers, 3 tutorial abstracts, and one panel abstract were carefully reviewed and selected from 186 submissions. The papers are organized in topical sections on uncertain data and ranking, sensor networks, graphs, RFID and data streams, skyline and rising stars, parallel and distributed processing, mining and analysis, XML query, privacy, XML keyword search and ranking, Web and Web services, XML data processing, and multimedia.

Data Mining, Southeast Asia Edition


Author: Jiawei Han,Jian Pei,Micheline Kamber
Publisher: Elsevier
ISBN: 9780080475585
Category: Computers
Page: 800
View: 7878

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Our ability to generate and collect data has been increasing rapidly. 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. 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. 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. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. 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. 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 Complete classroom support for instructors at www.mkp.com/datamining2e companion site

Knowledge Discovery in Inductive Databases

5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers
Author: Saso Dzeroski,Jan Struyf
Publisher: Springer
ISBN: 3540755497
Category: Computers
Page: 301
View: 3881

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This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.

Advanced Web Technologies and Applications

6th Asia-Pacific Web Conference, APWeb 2004, Hangzhou, China, April 14-17, 2004, Proceedings
Author: Jeffrey Xu Yu,Xuemin Lin,Hongjun Lu,Yanchun Zhang
Publisher: Springer
ISBN: 354024655X
Category: Computers
Page: 938
View: 7417

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The Asia-Paci?c region has emerged in recent years as one of the fastest g- wing regions in the world in the use of Web technologies as well as in making signi?cant contributions to WWW research and development. Since the ?rst Asia-Paci?c Web conference in 1998, APWeb has continued to provide a forum for researchers, professionals, and industrial practitioners from around the world to share their rapidly evolving knowledge and to report new advances in WWW technologies and applications. APWeb 2004 received an overwhelming 386 full-paper submissions, including 375 research papers and 11 industrial papers from 20 countries and regions: A- tralia,Canada,China,France,Germany,Greece,HongKong,India,Iran,Japan, Korea, Norway, Singapore, Spain, Switzerland, Taiwan, Turkey, UK, USA, and Vietnam. Each submission was carefully reviewed by three members of the p- gram committee. Among the 386 submitted papers, 60 regular papers, 24 short papers, 15 poster papers, and 3 industrial papers were selected to be included in the proceedings. The selected papers cover a wide range of topics including Web services, Web intelligence, Web personalization, Web query processing, Web - ching, Web mining, text mining, data mining and knowledge discovery, XML database and query processing, work?ow management, E-commerce, data - rehousing, P2P systems and applications, Grid computing, and networking. The paper entitled “Towards Adaptive Probabilistic Search in Unstructured P2P - stems”, co-authored by Linhao Xu, Chenyun Dai, Wenyuan Cai, Shuigeng Zhou, and Aoying Zhou, was awarded the best APWeb 2004 student paper.

Data Mining: Concepts and Techniques


Author: Jiawei Han,Jian Pei,Micheline Kamber
Publisher: Elsevier
ISBN: 9780123814807
Category: Computers
Page: 744
View: 2389

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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

Next Generation of Data Mining


Author: Hillol Kargupta,Jiawei Han,Philip S. Yu,Rajeev Motwani,Vipin Kumar
Publisher: CRC Press
ISBN: 9781420085877
Category: Computers
Page: 601
View: 5769

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Drawn from the US National Science Foundation’s Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Data Mining and Analysis

Fundamental Concepts and Algorithms
Author: Mohammed J. Zaki,Wagner Meira, Jr
Publisher: Cambridge University Press
ISBN: 0521766338
Category: Computers
Page: 562
View: 8561

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A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications

Concepts, Methodologies, Tools, and Applications
Author: Wang, John
Publisher: IGI Global
ISBN: 159904952X
Category: Technology & Engineering
Page: 4092
View: 6678

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In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.

Machine Learning for Data Streams

with Practical Examples in MOA
Author: Albert Bifet,Ricard Gavaldà,Geoff Holmes,Bernhard Pfahringer
Publisher: MIT Press
ISBN: 0262346052
Category: Computers
Page: 288
View: 9225

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A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.