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Applying the Immunological Network Concept to Clustering Document Collections

Applying the Immunological Network Concept to Clustering Document Collections

Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
Copyright: © 2009 |Pages: 20
ISBN13: 9781605663104|ISBN10: 1605663107|EISBN13: 9781605663111
DOI: 10.4018/978-1-60566-310-4.ch008
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MLA

Ciesielski, Krzysztof, et al. "Applying the Immunological Network Concept to Clustering Document Collections." Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies, edited by Hongwei Mo, IGI Global, 2009, pp. 160-179. https://doi.org/10.4018/978-1-60566-310-4.ch008

APA

Ciesielski, K., Klopotek, M. A., & Wierzchon, S. T. (2009). Applying the Immunological Network Concept to Clustering Document Collections. In H. Mo (Ed.), Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies (pp. 160-179). IGI Global. https://doi.org/10.4018/978-1-60566-310-4.ch008

Chicago

Ciesielski, Krzysztof, Mieczyslaw A. Klopotek, and Slawomir T. Wierzchon. "Applying the Immunological Network Concept to Clustering Document Collections." In Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies, edited by Hongwei Mo, 160-179. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-310-4.ch008

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Abstract

In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large collection of documents. Particularly a hierarchical, topic-sensitive approach is proposed; it appears to be a robust solution, both in terms of time and space complexity, to the problem of scalability of document map generation process. The approach relies upon extraction of a hierarchy of concepts, that is almost homogenous groups of documents described by unique sets of terms. To represent the content of each context a modified version the aiNet algorithm is employed; it was chosen because of its flexibility in representing natural clusters existing in a training set. To fasten the learning phase, a smart method of initialization of the immune memory was proposed as well as further modifications of the entire algorithm were introduced. Careful evaluation of the effectiveness of the novel text clustering procedure is presented in section reporting experiments.

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