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The library obtained due to my Ph.D. project.
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In addition to topological data analysis, unsupervised and supervised learning, the project ∂SIML also uses basics of algebraic topology to identify successful schemes. Currently under construction and constantly expanded. A documentation with the description and the corresponding functionality is also created here.

The Machine Learning Library

This repository provides a library for data analysis using clustering algorithms and algorithms for processing functional dependencies in the context of database technologies. The aim is to create a library that enables the development of a prototype for the implementation of automatic or semi-automatic schema inference. When we talk about schema inference, we think of a data stream, or a data set, that initially exists without defined relationtypes. From this, we would like to obtain a suitable schema using clustering techniques in combination with functional dependencies and normalization in order to support the database user.


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