Pattern Discovery Using Sequence Data Mining: Applications and Studies
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Pattern Discovery Using Sequence Data Mining: Applications and Studies

Pradeep Kumar, P. Radha Krishna, S. Bapi Raju
Indexed In: SCOPUS View 2 More Indices
Release Date: September, 2011|Copyright: © 2012 |Pages: 286
DOI: 10.4018/978-1-61350-056-9
ISBN13: 9781613500569|ISBN10: 1613500564|EISBN13: 9781613500576
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Description & Coverage
Description:

Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios.

Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Classification of Biological Sequences
  • Kernel Methods and Classification of Sequential Patterns
  • Kinase Sequence Mining for Drug Discovery
  • Mining Sequential Patterns from Weblogs
  • Mining Statistically Significant Substrings
  • Pattern Discovery for Architecture Simulation
  • Quantization Based Sequence Generation
  • Reverse Nearest Neighbor Search for Multimedia Data
  • Video Stream Mining for On-Road Traffic Analysis
Reviews & Statements

Computer scientists and engineers explain some of the ways that data in the form of sequences can be mined not only to find a particular value or event at a particular time, but also to reveal relationships between such values or events.

– SciTech Book News, Book News Inc., December 2011

This book can be useful to academic researchers and graduate students interested in data mining in general and in sequence data mining in particular, and to scientists and engineers working in fields where sequence data mining is involved, such as bioinformatics, genomics, Web services, security, and financial data analysis.

– Pradeep Kumar, Indian Institute of Management, India; P. Radha Krishna, Infosys Technologies Limited, India; and S. Bapi Raju, University of Hyderabad, India
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Editor/Author Biographies
Pradeep Kumar obtained his PhD from the Department of Computer and Information Sciences, University of Hyderabad, India. He also holds an MTech in Computer Science and BSc (Engg) in Computer Science and Engg. Currently, he is working as an Assistant Professor with Indian Institute of Management, Lucknow, India. His research interest includes data mining, soft computing and network security.
P. Radha Krishna is a Principal Research Scientist at Software Engineering and Technology Labs, Infosys Technologies Limited, Hyderabad, India. Prior to joining Infosys, Dr. Krishna was a Faculty Member at the Institute for Development and Research in Banking Technology (IDRBT) and a scientist at National Informatics Centre, India. His research interests include data warehousing, data mining, and electronic contracts and services. He authored five books and has more than eighty publications.
S. Bapi Raju obtained BTech (EE) from Osmania University, India, and his MS and PhD from University of Texas at Arlington, USA. He has over 12 years of teaching and research experience in neural networks, machine learning, and artificial intelligence and their applications. Currently he is a Professor in the Department of Computer and Information Sciences, as well as Associate Coordinator, Centre for Neural and Cognitive Sciences at University of Hyderabad. He has over 50 publications (journal / conference) in these areas. His main research interests include biological and artificial neural networks, neural and cognitive modelling, machine learning, pattern recognition, neuroimaging, and bioinformatics. He is a member of ACM, Society for Neuroscience, Cognitive Science Society, and a Senior Member of IEEE.
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