Data Mining Patterns: New Methods and Applications

Data Mining Patterns: New Methods and Applications

Pascal Poncelet (Ecole des Mines d'Ales, France), Florent Masseglia (Project AxIS-INRIA, France) and Maguelonne Teisseire (Universite Montpellier, France)
Indexed In: SCOPUS
Release Date: August, 2007|Copyright: © 2008 |Pages: 324
DOI: 10.4018/978-1-59904-162-9
ISBN13: 9781599041629|ISBN10: 1599041626|EISBN13: 9781599041643
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Description & Coverage

Since the introduction of the Apriori algorithm a decade ago, the problem of mining patterns is becoming a very active research area, and efficient techniques have been widely applied to the problems either in industry or science. Currently, the data mining community is focusing on new problems such as: mining new kinds of patterns, mining patterns under constraints, considering new kinds of complex data, and real-world applications of these concepts.

Data Mining Patterns: New Methods and Applications provides an overall view of the recent solutions for mining, and also explores new kinds of patterns. This book offers theoretical frameworks and presents challenges and their possible solutions concerning pattern extractions, emphasizing both research techniques and real-world applications. Data Mining Patterns: New Methods and Applications portrays research applications in data models, techniques and methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming, incremental mining, and many other topics.


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

  • Bi-directional constraint pushing
  • Class-labeled data
  • Data Visualization
  • Frequent pattern mining
  • Hierarchical Bayesian mixed-membership models
  • Latent patterns
  • Metric methods in data mining
  • Mining hyperclique patterns
  • Mining XML documents
  • Noisy document streams
  • Pattern discovery in biosequences
  • Social Network Mining
  • Spatio-textual association rules
  • Summarizing data cubes
  • Topic and cluster evolution
Reviews and Testimonials

Since data-streams are contigous, high speed and unbounded, it is impossible to mine patterns by using traditional algorithms requiring multiple scans and new approaches have to proposed.

– Pascal Poncelet, Ecole des Mines d’ALES
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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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