By Paul R. Cohen, Niall M. Adams, Michael R. Berthold

This ebook constitutes the refereed complaints of the ninth foreign convention on clever information research, IDA 2010, held in Tucson, AZ, united states in may possibly 2010. The 21 revised papers provided including 2 invited papers have been rigorously reviewed and chosen from greater than forty submissions. All present points of clever info research are addressed, quite clever aid for modeling and studying advanced, dynamical structures. themes coated are end-to-end software program platforms; modeling advanced structures comparable to gene regulatory networks, financial platforms, ecological structures, assets corresponding to water, and dynamical social structures corresponding to on-line groups; and robustness, scaling homes and different usability concerns.

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Additional resources for Advances in Intelligent Data Analysis IX: 9th International Symposium, IDA 2010, Tucson, AZ, USA, May 19-21, 2010. Proceedings

Sample text

However, this approach can be straightforwardly generalized to sequences of itemsets. The constraint-based pattern mining framework is a powerful paradigm to discover new highly valuable knowledge [13]. Constraints provide a focus on the most promising knowledge by reducing the number of extracted patterns to those of potential interest for the user. More precisely, constraint-based mining task selects all the sequential patterns included in SDB and satisfying a predicate which is called constraint.

C Springer-Verlag Berlin Heidelberg 2010 Recursive Sequence Mining to Discover Named Entity Relations 31 entity relations. Sequence mining, in particular sequential pattern mining [1], is a well-known data mining technique that allows to extract regularities in a sequence database. The recursive pattern mining [4] and the constraint-based paradigm [14] enable to give prominence to the most significant patterns. As we will see (Section 2), these techniques have suitable properties for our goal. The contribution of this work is twofold.

For that purpose, we propose a new data mining approach based on recursive sequence mining. The contribution of this work is twofold. First, we present a method based on a cross-fertilization of sequence mining under constraints and recursive pattern mining to produce a user-manageable set of linguistic information extraction rules. Moreover, unlike most works from the state-of-the-art in natural language processing, our approach does not need syntactic parsing of the sentences neither resource except the training data.

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