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There is a growing interest in learning data topology, from theoretical results to real-world applications. Topological learning is a very large field including manifold learning, computational geometry, etc. This workshop aims at bringing new ideas and research fields to the area of topological learning.

The aim of this workshop will be to address issues related to the concepts of learning data topology. Our goal will be to attract papers dealing with each step of this field. Actually, learning data topology within the KDD process implies to work on every step, starting from the preprocessing (e.g. structuring and organizing) to the visualization and interpretation of the results, via the data mining methods themselves.