CALL FOR CHAPTERS
Submission Deadline of Chapter Proposals: December 15, 2005 

Data Mining Patterns: New Methods and Applications
A book to be published in 2007 by Idea Group Inc.
Edited by:
Florent Masseglia (INRIA Sophia Antipolis)
Pascal Poncelet (EMA/LGI2P)
Maguelonne Teisseire (LIRMM)

Introduction
Since its definition, a decade ago, the problem of mining patterns is becoming a very active research area and efficient techniques have been widely applied to problems either in industry, government or science. Nowadays, the data mining community has focused on new challenging problems such as:

Mining new kinds of patterns :
From the initial definition and motivated by real-applications, the problem of mining patterns not only addresses the finding of item sets but also more and more complex patterns. As the number of digital data is always growing, the problem of the efficiency of mining such patterns becomes more and more attractive. For instance, in the biological networks context, we have to deal with common patterns of cellular interactions, organization of functional modules, relationships and interaction between sequences, and patterns of genes regulation.

Mining patterns under constraints :
In order to efficiently aid decision making and for effectiveness consideration, constraints become more and more essential in many applications. Indeed, an unconstrained mining can produce such a large number of patterns that it may be intractable in some domains. Furthermore, the growing consensus that the end user is no more interested by a set of all patterns verifying selection criteria led to demand for novel strategies for extracting useful, even approximate knowledge.

Considering new kinds of complex data for pattern mining :
Close to the former point, patterns are clearly related to stored data. Nowadays, we have to consider more than numerical or symbolic data. There is a critical need for managing, querying and mining complex data. Furthermore, open problems remain according to the relevance of extracted patterns when considering missing, noisy or incomplete data.

Highly dynamic and streaming data :
Many real-world applications data are no more appropriately handled by traditional static databases since data arrives sequentially in the form of continuous rapid streams. Such applications can be stock tickers, network traffic measurements, transaction flows in retail chains, click streams, fraud detection, road traffic or sensor networks and telecommunications call records, etc. Since data-streams are contiguous, high speed and unbounded, it is impossible to mine patterns by using traditional algorithms requiring multiple scans and new approaches have to proposed.

Real-world applications :
Industrial and real-world applications of data mining are getting more and more challenging. We will consider papers related to real-world applications of data mining since this context is generally synonymous of recent technologies, new kinds of data, and new kinds of patterns.

The objective of this book is to provide an overall view of the recent existing solutions for mining new kinds of patterns. It aims at providing theoretical frameworks and presenting challenges and their possible solutions concerning patterns extraction with an emphasis on both research techniques and real-world applications.

The Target audience
The main topics addressed by the book will be useful for computing science students, application developers, and business professionals, as well as researchers involved in data mining. It will become a useful guide for researchers, practitioners and graduate-level students interested in learning the state-of-the-art development in mining cutting-edge industrial, complex or dynamic data. Furthermore, the book will constitute a resource of possible solutions and technologies that can be applied to recent problems in industry, science, engineering and government.

Recommended topics include, but are not limited to:

• Data models, techniques and methodologies for mining patterns
• Bio-KDD patterns
• Multi-relational and multi-dimensional pattern mining
• XML, graph, tree and sequence mining
• Fuzzy data mining
• Mining data streams
• Incremental mining
• Mining time series
• Mining links or networks
• Mining anonymized data
• Images/video Mining
• Mining music and audio
• Scalability of algorithms for mining patterns
• Integrating constraints and knowledge in the KDD process
• Usage of patterns
• Quality of extracted patterns
• Visualization techniques
• Applications: click-stream, fraud detection, semantic web mining, governmental data, text mining

Important Dates
December 15, 2005 : Submission of proposals for chapters
December 31, 2005 : Notification of proposal acceptance/rejection
April 30, 2006 : Submission of full chapters
June 15, 2006 : Notification of the chapter reviews
July 15, 2006 : Submission of revised chapters
August 31, 2006 : Notification of final acceptance/rejection
September 15, 2006 : Final version of chapters

CONTACT
This book will be edited by:

• Florent Masseglia (INRIA Sophia Antipolis)
• Pascal Poncelet (EMA/LGI2P)
• Maguelonne Teisseire (LIRMM)

Please send inquiries and submissions to dmpbook@sophia.inria.fr
For further details please visit the book web page: http://www-sop.inria.fr/axis/dmpbook/