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aiNet: An Artificial Immune Network for Data Analysis
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Author(s): Leandro Nunes de Castro (State University of Campinas, Brazil)and Fernando J. Von Zuben (State University of Campinas, Brazil)
Copyright: 2002
Pages: 30
Source title:
Data Mining: A Heuristic Approach
Source Author(s)/Editor(s): Hussein A. Abbass (University of New South Wales, Australia), Ruhul Sarker (University of New South Wales, Australia)and Charles S. Newton (University of New South Wales, Australia)
DOI: 10.4018/978-1-930708-25-9.ch012
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Abstract
This chapter shows that some of the basic aspects of the natural immune system discussed in the previous chapter can be used to propose a novel artificial immune network model with the main goals of clustering and filtering crude data sets described by high-dimensional samples. Our aim is not to reproduce with confidence any immune phenomenon, but demonstrate that immune concepts can be used as inspiration to develop novel computational tools for data analysis. As important results of our model, the network evolved will be capable of reducing redundancy and describing data structure, including their spatial distribution and cluster interrelations. Clustering is useful in several exploratory pattern analyses, grouping, decision-making and machine-learning tasks, including data mining, knowledge discovery, document retrieval, image segmentation and automatic pattern classification. The data clustering approach was implemented in association with hierarchical clustering and graphtheoretical techniques, and the network performance is illustrated using several benchmark problems. The computational complexity of the algorithm and a detailed sensitivity analysis of the user-defined parameters are presented. A trade-off among the proposed model for data analysis, connectionist models (artificial neural networks) and evolutionary algorithms is also discussed.
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