| Abstract: |
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Topological Data Analysis (TDA) is a modern approach for analysing complex
datasets using tools from algebraic topology, the branch of mathematics
concerned with studying the shape of spaces. Unlike conventional statistics, which
often assumes linear or low-dimensional structures, TDA is designed to uncover
hidden patterns and global organisation in high-dimensional and noisy data. There
are two main approaches to TDA: Persistent Homology (PH) and Mapper. In this
talk, we will focus on Mapper, which creates a simplified graph-based
representation of high-dimensional data. It works by partitioning the dataset,
analysing local structures, and connecting them into a network called a Mapper
graph. We end by discussing an application for breast cancer analysis.
\end{document} |
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