Clustering matrix-object data by correlational structure as proxy causal signals.

Area of Science:

• Data Science
• Machine Learning
• Statistical Modeling

Background:

• Existing matrix-object clustering methods often ignore critical within-object dependencies.
• This oversight leads to reduced interpretability and the mixing of heterogeneous data regimes.
• A need exists for methods that can disentangle these regimes effectively.

Purpose of the Study:

• To propose a new clustering approach for matrix-object data that explicitly accounts for intra-object dependencies.
• To enhance the interpretability of clustering results by separating distinct data regimes.
• To utilize correlational structures as proxy signals for causal inference prior to formal discovery.

Main Methods:

• The proposed method transforms each object into a rank-based correlation representation.
• This transformation allows for standard distance-based clustering techniques to be applied.
• Intra-object correlational structure is used as a proxy for causal signals to pre-separate regimes.

Main Results:

• The novel clustering approach yields stable and interpretable clusters across synthetic and real-world datasets.
• The method effectively reduces the mixing of heterogeneous data regimes.
• Demonstrated improved separation of underlying data structures compared to existing methods.

Conclusions:

• The developed matrix-object clustering technique offers a robust way to handle complex datasets with multiple records per object.
• By using correlational patterns as proxy signals, the method enhances regime separation and interpretability.
• The study highlights the importance of considering intra-object dependencies for accurate data analysis, while cautioning that correlation does not imply causation.