Frequent Pattern Mining

Author: Charu C. Aggarwal

Publisher: Springer

ISBN:

Category: Computers

Page: 471

View: 231

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.

Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories

Author: Berkay Aydin

Publisher: Springer

ISBN:

Category: Computers

Page: 106

View: 396

This SpringerBrief provides an overview within data mining of spatiotemporal frequent pattern mining from evolving regions to the perspective of relationship modeling among the spatiotemporal objects, frequent pattern mining algorithms, and data access methodologies for mining algorithms. While the focus of this book is to provide readers insight into the mining algorithms from evolving regions, the authors also discuss data management for spatiotemporal trajectories, which has become increasingly important with the increasing volume of trajectories. This brief describes state-of-the-art knowledge discovery techniques to computer science graduate students who are interested in spatiotemporal data mining, as well as researchers/professionals, who deal with advanced spatiotemporal data analysis in their fields. These fields include GIS-experts, meteorologists, epidemiologists, neurologists, and solar physicists.

Data Mining, Southeast Asia Edition

Author: Jiawei Han

Publisher: Elsevier

ISBN:

Category: Computers

Page: 800

View: 529

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

Next Generation of Data Mining

Author: Hillol Kargupta

Publisher: CRC Press

ISBN:

Category: Computers

Page: 601

View: 778

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.

Advanced Data Mining and Applications

4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008, Proceedings

Author: Changjie Tang

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 759

View: 624

This book constitutes the refereed proceedings of the 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, held in Chengdu, China, in October 2008. The 35 revised full papers and 43 revised short papers presented together with the abstract of 2 keynote lectures were carefully reviewed and selected from 304 submissions. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining and feature original research results in data mining, spanning applications, algorithms, software and systems, and different applied disciplines with potential in data mining.

Data Mining in Bioinformatics

Author: Jason T. L. Wang

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 340

View: 963

Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Advances in Knowledge Discovery and Data Mining

13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings

Author: Thanaruk Theeramunkong

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 1076

View: 179

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandData Mining hasbeen held every year from 1997. PAKDD 2009, the 13th in the series, was held in Bangkok, Thailand during April 27-30, 2008. PAKDD is a major inter- tional conference in the areas of data mining (DM) and knowledge discovery in database (KDD). It provides an international forum for researchers and ind- try practitioners to share their new ideas, original research results and prac- cal development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acqui- tion and automatic scienti?c discovery, data visualization, causal induction and knowledge-based systems. ForPAKDD2009,wereceived338researchpapersfromvariouscountriesand regions in Asia, Australia, North America, South America, Europe, and Africa. Every submission was rigorously reviewed by at least three reviewers with a doubleblindprotocol.Theinitialresultswerediscussedamongthereviewersand ?nally judged by the ProgramCommittee Chairs. When there was a con?ict, an additionalreviewwasprovidedbytheProgramCommitteeChairs.TheProgram Committee members were deeply involved in the highly selective process. As a result, only 39 papers (approximately 11.5% of the 338 submitted papers) were accepted as regular papers, 73 papers (21.6% of them) were accepted as short papers.

Advanced Data Mining and Applications

6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings

Author: Longbing Cao

Publisher: Springer

ISBN:

Category: Computers

Page: 628

View: 895

With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.

Advances in Knowledge Discovery and Data Mining

10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings

Author: Wee Keong Ng

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 879

View: 591

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of data mining and knowledge discovery. This year marks the tenth anniversary of the successful annual series of PAKDD conferences held in the Asia Pacific region. It was with pleasure that we hosted PAKDD 2006 in Singapore again, since the inaugural PAKDD conference was held in Singapore in 1997. PAKDD 2006 continues its tradition of providing an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all aspects of KDD data mining, including data cleaning, data warehousing, data mining techniques, knowledge visualization, and data mining applications. This year, we received 501 paper submissions from 38 countries and regions in Asia, Australasia, North America and Europe, of which we accepted 67 (13.4%) papers as regular papers and 33 (6.6%) papers as short papers. The distribution of the accepted papers was as follows: USA (17%), China (16%), Taiwan (10%), Australia (10%), Japan (7%), Korea (7%), Germany (6%), Canada (5%), Hong Kong (3%), Singapore (3%), New Zealand (3%), France (3%), UK (2%), and the rest from various countries in the Asia Pacific region.

Advances in Knowledge Discovery and Data Mining

9th Pacific-Asia Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings

Author: Tu Bao Ho

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 864

View: 289

This book constitutes the refereed proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2005, held in Hanoi, Vietnam, in May 2005. The 48 revised full papers and 49 revised short papers presented together with abstracts or extended abstracts of 3 invited talks were carefully reviewed and selected from 327 submissions. The papers are organized in topical sections on theoretical foundations, association rules, biomedical domains, classification and ranking, clustering, dynamic data mining, graphical model discovery, high dimensional data, integration of data warehousing, knowledge management, machine learning, novel algorithms, spatial data, temporal data, and text and Web data mining.