In this chapter we focus on some problems concerning application of an immune-based algorithm to extraction and visualization of cluster structure. Particularly a hierarchical, topic-sensitive approach is proposed; it appears to be a robust solution to the problem of scalability of document map generation process (both in terms of time and space complexity). This approach relies upon extraction of a hierarchy of concepts, i.e. almost homogenous groups of documents described by unique sets of terms. To represent the content of each context a modified version the aiNet [9] algorithm is employed; it was chosen because of its natural ability to represent internal patterns existing in a training set. Careful evaluation of the effectiveness of the novel text clustering procedure is presented in section reporting experiments.
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Wierzchoń, S.T., Ciesielski, K., Kłopotek, M.A. (2008). Scalability and Evaluation of Contextual Immune Model for Web Mining. In: Hassanien, AE., Abraham, A., Kacprzyk, J. (eds) Computational Intelligence in Multimedia Processing: Recent Advances. Studies in Computational Intelligence, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76827-2_15
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