STORM - A Novel Information Fusion and Cluster Interpretation Technique

TL;DR

This paper introduces STORM, an immune-inspired data fusion technique that integrates diverse data types to enhance unsupervised analysis and visualization, aiding better understanding and knowledge discovery.

Contribution

The paper presents a novel immune-inspired method for fusing disparate data types, improving interpretability in unsupervised data analysis.

Findings

Enhanced visual understanding of data through fusion

Effective integration of multiple data sources

Implications for exploratory data analysis

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.