Master Thesis Exploring Semi-Quantitative Methods for Interpreting Correlation Matrices

In many fields of science and engineering correlation matrices are used to explore relationships between variables in complex systems. These matrices often serve as a foundation for building predictive models identifying clusters or guiding decision-making. However in some cases such as early-stage data collection aggregated datasets or constrained measurement environments the available correlation data may be of limited resolution. This means that while the matrix may reveal general patterns or associations it lacks the precision or granularity required for robust mathematical modeling.

Despite these limitations correlation matrices can still offer valuable insights. They may highlight broad trends suggest potential groupings or indicate areas worth further investigation. Traditional visualization techniques like heatmaps can help surface these patterns but they often fall short of enabling deeper semi-quantitative interpretation.

This diploma work aims to explore the space between visual exploration and formal modeling investigating how structured but coarse correlation data can be used to extract meaningful insights. By combining advanced visualization methods with semi-formal analytical tools such as fuzzy logic graph-based representations or heuristic modeling the project seeks to develop a framework for working effectively with imperfect but informative data.

Project Aim
To investigate and develop methods that bridge the gap between visual exploration and mathematical modeling when working with correlation data of limited resolution quality and depth.

Key Components of the Project
Characterize Existing Data
- Analyze the structure resolution and quality of the available correlation data.

Explore Visualization Techniques
- Explore network graphs clustering and dimensionality reduction methods.
- Use graph-based representations to capture structure without assuming precise numerical relationships.

Qualitative Modeling
- Investigate fuzzy logic qualitative reasoning or probabilistic graphical models to represent uncertain or imprecise relationships.

Application and Evaluation
- Apply the developed methods to a real or synthetic dataset.
- Compare the insights gained through visual semi-quantitative and formal modeling approaches.

Expected Outcome
An alternative approach to model correlations suitable for visualization extraction hypothesis generation and quantitative analysis including a concrete framework which can be used as basis for future modelling.

Timeframe and Schedule
Full time work one semester.

The project require knowledge and studies in Engineering Physics.

Selection is made continuously send in your application today!

Contact Information Tetra Pak D&T Industrial Base Engineering
Fredrik Andersson
Systems Engineer A
Mail:
Phone: 046-362614

Erik Hamberg
Mail:
Manager Converting Process Integration
Phone: 046-363410