Poncelet P., Teisseire M., Masseglia F. Data Mining Patterns. New Methods and Applications

IGI Global, 2008, -324 p.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. 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. For instance, new approaches need to be defined for mining graphs or trees in applications dealing with complex data such as XML documents, correlated alarms or biological networks. As the number of digital data are always growing, the problem of the efficiency of mining such patterns becomes more and more attractive.
One of the first areas dealing with a large collection of digital data is probably text mining. It aims at analyzing large collections of unstructured documents with the purpose of extracting interesting, relevant and nontrivial knowledge. However, patterns became more and more complex, and led to open problems. 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. In the same way, multidimensional pattern mining has also been defined, and a lot of open questions remain regarding the size of the search space or to effectiveness consideration. If we consider social network in the Internet, we would like to better understand and measure relationships and flows between people, groups and organizations. Many real-world applications data are no longer appropriately handled by traditional static databases since data arrive sequentially in rapid, continuous streams. 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 be proposed.
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 patterns verifying selection criteria led to demand for novel strategies for extracting useful, even approximate knowledge. The goal of this book is to provide an overall view of the existing solutions for mining new kinds of patterns. It aims at providing theoretical frameworks and presenting challenges and possible solutions concerning pattern extraction with an emphasis on both research techniques and real-world applications.Metric Methods in Data Mining
Bi-Directional Constraint Pushing in Frequent Pattern Mining
Mining Hyperclique Patterns: A Summary of Results
Pattern Discovery in Biosequences: From Simple to Complex Patterns
Finding Patterns in Class-Labeled Data Using Data Visualization
Summarizing Data Cubes Using Blocks
Social Network Mining from the Web
Discovering Spatio-Textual Association Rules in Document Images
Mining XML Documents
Topic and Cluster Evolution Over Noisy Document Streams
Discovery of Latent Patterns with Hierarchical Bayesian Mixed-Membership Models and the Issue of Model Choice