Sequence Data Mining

Author : Guozhu Dong
Publisher : Springer Science & Business Media
ISBN 13 : 0387699376
Total Pages : 160 pages
Book Rating : 4.70/5 ( download)

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Download or read book Sequence Data Mining written by Guozhu Dong and published by Springer Science & Business Media. This book was released on 2007-10-31 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.

Author : Pradeep Kumar
Publisher :
ISBN 13 : 9781613500583
Total Pages : 272 pages
Book Rating : 4.80/5 ( download)

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Download or read book Pattern Discovery Using Sequence Data Mining written by Pradeep Kumar and published by . This book was released on 2011-07-01 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"--

Author : Wei Wang
Publisher : Springer Science & Business Media
ISBN 13 : 0387242473
Total Pages : 174 pages
Book Rating : 4.77/5 ( download)

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Download or read book Mining Sequential Patterns from Large Data Sets written by Wei Wang and published by Springer Science & Business Media. This book was released on 2005-07-26 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.

Author : Khosrow-Pour D.B.A., Mehdi
Publisher : IGI Global
ISBN 13 : 1799834778
Total Pages : 950 pages
Book Rating : 4.79/5 ( download)

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Download or read book Handbook of Research on Modern Educational Technologies, Applications, and Management written by Khosrow-Pour D.B.A., Mehdi and published by IGI Global. This book was released on 2020-07-10 with total page 950 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology and technological advancements become a more prevalent and essential aspect of daily and business life, educational institutions must keep pace in order to maintain relevance and retain their ability to adequately prepare students for their lives beyond education. Such institutions and their leaders are seeking relevant strategies for the implementation and effective use of new and upcoming technologies and leadership strategies to best serve students and educators within educational settings. As traditional education methods become more outdated, strategies to supplement and bolster them through technology and effective management become essential to the success of institutions and programs. The Handbook of Research on Modern Educational Technologies, Applications, and Management is an all-encompassing two-volume scholarly reference comprised of 58 original and previously unpublished research articles that provide cutting-edge, multidisciplinary research and expert insights on advancing technologies used in educational settings as well as current strategies for administrative and leadership roles in education. Covering a wide range of topics including but not limited to community engagement, educational games, data management, and mobile learning, this publication provides insights into technological advancements with educational applications and examines forthcoming implementation strategies. These strategies are ideal for teachers, instructional designers, curriculum developers, educational software developers, and information technology specialists looking to promote effective learning in the classroom through cutting-edge learning technologies, new learning theories, and successful leadership tactics. Administrators, educational leaders, educational policymakers, and other education professionals will also benefit from this publication by utilizing the extensive research on managing educational institutions and providing valuable training and professional development initiatives as well as implementing the latest administrative technologies. Additionally, academicians, researchers, and students in areas that include but are not limited to educational technology, academic leadership, mentorship, learning environments, and educational support systems will benefit from the extensive research compiled within this publication.

Author : Pradeep Kumar
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.43/5 ( download)

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Download or read book Pattern Discovery Using Sequence Data Mining written by Pradeep Kumar and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"-- Provided by publisher.

Author : Jan Zytkow
Publisher : Springer Science & Business Media
ISBN 13 : 3540664904
Total Pages : 608 pages
Book Rating : 4.01/5 ( download)

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Download or read book Principles of Data Mining and Knowledge Discovery written by Jan Zytkow and published by Springer Science & Business Media. This book was released on 1999-09-01 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.

Author : Richard Durbin
Publisher : Cambridge University Press
ISBN 13 : 113945739X
Total Pages : 372 pages
Book Rating : 4.92/5 ( download)

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Download or read book Biological Sequence Analysis written by Richard Durbin and published by Cambridge University Press. This book was released on 1998-04-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.

Author : Mokrane Bouzeghoub
Publisher : Springer Science & Business Media
ISBN 13 : 9783540610571
Total Pages : 660 pages
Book Rating : 4.7X/5 ( download)

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Download or read book Advances in Database Technology EDBT '96 written by Mokrane Bouzeghoub and published by Springer Science & Business Media. This book was released on 1996-03-18 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the Fifth International Conference on Extending Database Technology, EDBT'96, held in Avignon, France in March 1996. The 31 full revised papers included were selected from a total of 178 submissions; also included are some industrial-track papers, contributed by partners of several ESPRIT projects. The volume is organized in topical sections on data mining, active databases, design tools, advanced DBMS, optimization, warehousing, system issues, temporal databases, the web and hypermedia, performance, workflow management, database design, and parallel databases.

Author : Jason T. L. Wang
Publisher : Springer Science & Business Media
ISBN 13 : 1846280591
Total Pages : 340 pages
Book Rating : 4.97/5 ( download)

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Download or read book Data Mining in Bioinformatics written by Jason T. L. Wang and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Bater Makhabel
Publisher : Packt Publishing Ltd
ISBN 13 : 178829081X
Total Pages : 651 pages
Book Rating : 4.14/5 ( download)

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Download or read book R: Mining spatial, text, web, and social media data written by Bater Makhabel and published by Packt Publishing Ltd. This book was released on 2017-06-19 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands-on experience of generating insights from social media data. You will get detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Learning Data Mining with R by Bater Makhabel R Data Mining Blueprints by Pradeepta Mishra Social Media Mining with R by Nathan Danneman and Richard Heimann Style and approach A complete package with which will take you from the basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining—social media mining.