STORM - a novel information fusion and cluster interpretation technique

Feyereisl, Jan; Aickelin, Uwe

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.

Citation

Feyereisl, J., & Aickelin, U. (2009, September). STORM - a novel information fusion and cluster interpretation technique. Presented at 10th International Conference, 2009, Burgos, Spain

• Presentation Conference Type: Edited Proceedings
• Conference Name: 10th International Conference, 2009
• Start Date: Sep 23, 2009
• End Date: Sep 26, 2009
• Publication Date: Jan 1, 2010
• Deposit Date: Aug 10, 2011
• Publicly Available Date: Aug 10, 2011
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 208–218
• Series Title: Lecture notes in computer science
• Series Number: 5788
• Series ISSN: 1611-3349
• Book Title: Intelligent Data Engineering and Automated Learning - IDEAL 2009
• ISBN: 9783642043932
• DOI: https://doi.org/10.1007/978-3-642-04394-9_26
• Public URL: https://nottingham-repository.worktribe.com/output/1012537
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-642-04394-9_26