Sequence Data Mining

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.

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.

"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"--

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.

"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"--

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.

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.

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.