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Authors
Advisor(s)
Abstract(s)
Triclustering, targeting the discovery of coherent subspaces within three-way data, is becoming increasingly
relevant in data science, especially for pattern discovery and knowledge acquisition from complex
datasets in the biomedical field. This technique can reveal hidden patterns such as putative regulatory
modules, disease progression profiles, and individuals with coherent behaviors. When applied to labeled
data, triclustering aids in class differentiation and supports real-world decision-making. However,
learning from 3W biomedical data is typically challenged by the rich temporal and heterogeneous nature,
having mixed-type features and different structure compositions. In response to these challenges,
this thesis establishes the foundations for pattern-centric 3W data analysis, focusing on triclustering for
temporal and heterogeneous three-way data, targeting both descriptive and predictive tasks. In this context,
this thesis includes six major contributions. It provides a literature review and comparative study
of current triclustering algorithms for temporal data, highlighting the strengths and weaknesses of existing
methods. It presents new tools to support the development and assessment of pattern discovery
approaches in descriptive and predictive contexts, including a new data generator capable of creating
heterogeneous three-way datasets with annotated triclustering solutions and benchmark datasets for comparative
evaluation. It proposes a novel approach to capture time-contiguous triclusters, enhancing the
search for temporal coherence. It introduces a new triclustering approach able to handle heterogeneous
data by applying sequential pattern mining principles to identify relevant patterns and derive triclusters
capturing temporal data dynamics. Additionally, it presents a new method for learning pattern-centric
predictors. Finally, it proposes an extension and integration of principles for learning from static and
temporal data structures. The developed methods were comprehensively validated in concrete real-world
clinical scenarios, showing promising results concerning two progressive diseases. They were used to
predict clinically relevant endpoints and identify disease-specific progression patterns, supporting medical
decisions and identifying significant patient profiles.
Description
Keywords
Triclustering Pattern Discovery Pattern-centric Predictive Models Multivariate Time-series Heterogeneous Clinical Data