Successes and New Directions in Data Mining

Successes and New Directions in Data Mining

Pascal Poncelet, Florent Masseglia, Maguelonne Teisseire
Indexed In: SCOPUS
Release Date: October, 2007|Copyright: © 2008 |Pages: 386
DOI: 10.4018/978-1-59904-645-7
ISBN13: 9781599046457|ISBN10: 1599046458|EISBN13: 9781599046471
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Description & Coverage
Description:

The problem of mining patterns is becoming a very active research area and efficient techniques have been widely applied to problems in industry, government, and science. From the initial definition and motivated by real-applications, the problem of mining patterns not only addresses the finding of itemsets but also more and more complex patterns.

Successes and New Directions in Data Mining addresses existing solutions for data mining, with particular emphasis on potential real-world applications. Capturing defining research on topics such as fuzzy set theory, clustering algorithms, semi-supervised clustering, modeling and managing data mining patterns, and sequence motif mining, this book is an indispensable resource for library collections.

Coverage:

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

  • Audio data structure
  • Data mining concepts, techniques, and evaluation methodologies
  • Data mining in Metabolomics
  • Fuzzy Set Theory
  • Mining Data Streams
  • Modeling and managing heterogeneous patterns
  • Motif mining
  • Pattern mining and clustering
  • Privacy Preservation
  • Structural information usage
  • XML Queries
Reviews & Statements

The goal of this book is to provide theoretical frameworks and present challenges and their possible solutions concerning knowledge extraction. It aims at providing an overall view of the recent existing solutions for data mining with a particular emphasis on the potential real-world applications.

– Florent Masseglia, Projet AxIS-INRIA, France

The present coverage documents the successful research endeavors in data mining today. This work is an excellent addition to research collections.

– CHOICE, Vol. 45, No. 09 (May 2008)

The book will be useful as a reference for researchers, practitioners, and students in fields related to data mining and data warehousing.

– Book News Inc. (2008)

The book presents chapters that are not only relevant to the data mining research community but also, in cases, introductory to new and necessary fields of reach pointing whenever possible future trend.

– Renato Cordeiro de Amorim, University of London, UK
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Editor/Author Biographies
Pascal Poncelet is a professor and the head of the data mining research group in the computer science department at the Ecole des Mines d’Alès in France. He is also co-head of the department. Professor Poncelet has previously worked as lecturer (1993-1994), as associate professor, respectively, in the Méditerranée University (1994-1999) and Montpellier University (1999 2001). His research interest can be summarized as advanced data analysis techniques for emerging applications. He is currently interested in various techniques of data mining with application in Web mining and text mining. He has published a large number of research papers in refereed journals, conference, and workshops, and been reviewer for some leading academic journals. He is also co-head of the French CNRS Group “I3” on data mining.
Florent Masseglia is currently a researcher for INRIA (Sophia Antipolis, France). He did research work in the Data Mining Group at the LIRMM (Montpellier, France) (1998-2002) and received a PhD in computer science from Versailles University, France (2002). His research interests include data mining (particularly sequential patterns and applications such as Web usage mining) and databases. He is a member of the steering committees of the French working group on mining complex data and the International Workshop on Multimedia Data. He has co-edited several special issues about mining complex or multimedia data. He also has co-chaired workshops on mining complex data and co-chaired the 6th and 7th editions of the International Workshop on Multimedia Data Mining in conjunction with the KDD conference. He is the author of numerous publications about data mining in journals and conferences and he is a reviewer for international journals.
Maguelonne Teisseire received a PhD in computing science from the Méditerranée University, France (1994). Her research interests focused on behavioral modeling and design. She is currently an assistant professor of computer science and engineering in Montpellier II University and Polytech’Montpellier, France. She is head of the Data Mining Group at the LIRMM Laboratory, Montpellier. Her interests focus on advanced data mining approaches when considering that data are time ordered. Particularly, she is interested in text mining and sequential patterns. Her research takes part on different projects supported by either National Government (RNTL) or regional projects. She has published numerous papers in refereed journals and conferences either on behavioral modeling or data mining.
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