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Feyereisl, Jan and Aickelin, Uwe (2010) STORM - a novel information fusion and cluster interpretation technique. In: Intelligent data engineering and automated learning -- IDEAL 2009:10th internatio conference, Bourgos, Spain, September 23-26, 2009: proceedings. Lecture notes in computer science (5788). Springer, Berlin, pp. 208-218. ISBN 9783642043949

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Abstract

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is

examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more

challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another

hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.

Item Type:	Book Section
RIS ID:	https://nottingham-repository.worktribe.com/output/1012537
Schools/Departments:	University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User:	Aickelin, Professor Uwe
Date Deposited:	10 Aug 2011 10:57
Last Modified:	04 May 2020 20:25
URI:	https://eprints.nottingham.ac.uk/id/eprint/1286

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