Download Advances in Intelligent Data Analysis IX: 9th International by Paul R. Cohen, Niall M. Adams, Michael R. Berthold PDF

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

ISBN-10: 3642130623

ISBN-13: 9783642130625

This booklet constitutes the refereed court cases of the ninth overseas convention on clever information research, IDA 2010, held in Tucson, AZ, united states in might 2010. The 21 revised papers awarded including 2 invited papers have been rigorously reviewed and chosen from greater than forty submissions. All present points of clever facts research are addressed, fairly clever help for modeling and studying advanced, dynamical platforms. themes coated are end-to-end software program structures; modeling complicated platforms akin to gene regulatory networks, financial structures, ecological structures, assets akin to water, and dynamical social structures similar 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

Given a minimum support threshold minsup, the problem of frequent sequential pattern mining is to find the complete set of sequential patterns whose support is greater than or equal to minsup. There are many other constraints highlighting the best sequential patterns with respect to the user objectives [14]. In this work, we will see that we use both syntactic constraints and constraints coming from linguistic information. 2 Recursive Pattern Mining Recursive pattern mining [4] is a process that gives prominence to the most significant patterns and filters the specific ones.

In addition to usual evaluations by using Precision and Recall measures, Section 4 shows that this small number of computed sequential patterns enables a validation by linguists. 2 Sequential Pattern Mining for Named Entity Relations For the extraction of sequential patterns as linguistic IE rules, the database is built from texts which contain relations and where the named entities are identified and replaced by the specific item Named Entity. e. a verb or a noun, and at least two named entities.

Second, we show in practice how to apply the computed rules to detect new relations between named entities, highlighting the interest of hybridization of data mining and natural language processing techniques in the discovery of knowledge. We illustrate our approach with the detection of gene interactions in biomedical literature. Keywords: Sequential Data, Recursive Mining, Named Entity Relations, Pattern Discovery, Natural Language Processing. 1 Introduction Due to the explosion of available textual data, text mining and Information Extraction (IE) from texts have become important topics of study in recent years.

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