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STORM - a novel information fusion and cluster interpretation technique

Feyereisl, Jan; Aickelin, Uwe

STORM - a novel information fusion and cluster interpretation technique Thumbnail


Authors

Jan Feyereisl

Uwe Aickelin



Contributors

Emilio Corchado
Editor

Hujun Yin
Editor

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.

Publication Date Jan 1, 2010
Deposit Date Aug 10, 2011
Publicly Available Date Aug 10, 2011
Peer Reviewed Peer Reviewed
Issue 5788
Series Title Lecture notes in computer science
Book Title Intelligent data engineering and automated learning -- IDEAL 2009:10th internatio conference, Bourgos, Spain, September 23-26, 2009: proceedings
ISBN 9783642043949
Public URL https://nottingham-repository.worktribe.com/output/1012537

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