By Anna Bartkowiak, Nickolay T. Trendafilov (auth.), Prof. Dr. Mieczysław A. Kłopotek, Prof. Dr. Sławomir T. Wierzchoń, Dr. Krzysztof Trojanowski (eds.)

This edited e-book includes articles approved for presentation in the course of the convention "Intelligent info structures 2005 (IIS 2005) - New traits in clever details Processing and internet Mining" held in Gdansk, Poland, on June 13-16, 2005. specified awareness is dedicated to the latest advancements within the components of man-made Immune platforms, se's, Computational Linguistics and information Discovery. the focal point of this publication can also be on new computing paradigms together with biologically encouraged tools, quantum computing, DNA computing, complex information research, new laptop studying paradigms, reasoning applied sciences, usual language processing and new optimization concepts.

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Example text

Influence of simple selection and Latent Semantic Analysis (LSA) on structures of such graphs is analyzed. Homepages and lists of publications are converted to a word frequency vector, filtered, weighted and similarity matrix between normalized vectors is used to create separate minimum sub-trees showing clustering of people’s interest. Results show that in this application simple selection of important keywords is as good as LSA but with much lower algorithmic complexity. 1 Introduction Maps presenting connections, associations, clustering or similarity are a popular way to present information.

Normalized term frequency tfij is obtained by dividing each column of the F matrix by the maximum element in that column: tfij = fij maxi (fij ) (1) Inverse document frequency idfj is a measure of uniqueness of term j. Let n be the number of all documents and dj a number of documents in which term j occurs, then: n idfj = log2 +1 (2) dj The weight wij of a term i in a document j is defined by: wij = tfij · idfj = fij maxi (fij ) log2 n dj +1 (3) Relations between documents may be derived and visualized calculating similarity between columns of the W = (wij )m×n matrix.

The reduced training set guarantees high generalization accuracy, sometimes only slightly worse and often even better then the accuracy achieved using the whole training set. Moreover, the IRA family are often competitive to other instance reduction algorithms. It can be also concluded that the classifier choice does not have a significant impact on performance of instance reduction algorithms. The future direction of research will focus on establishing decision rules for finding an appropriate representation level suitable for each cluster allowing different representation levels for different clusters.

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