International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Topological Data Analysis (TDA) is relatively new filed of Applied Mathematics that emerged rapidly last years. The main tool of Topological Data Analysis is Persistent Homology. Persistent Homology provides some topological characteristics of the datasets. In this paper we will discuss classification of digital images using their topological signatures computed with Persistent Homology. We will experiment on the Fashion-MNIST dataset. Using Topological Data Analysis, the classification was improved.

In this paper we introduce, analyze and apply persistent homology, one of the main algorithms of TDA, on some real data sets from the bio-medical field. Topological data analysis (TDA) is a field which is a synergy between mathematics, data science and computer science. The main goal of TDA is studying the shape of data using topological techniques. TDA proposes new algorithms that deal with these problems based on tools or concepts from algebraic topology and pure mathematics. We analyze the results and give a topological characterization of the dataset and propose to use them in future work.

In this paper we analyze and apply one of the main algorithms of TDA (Topological Data Analysis), Mapper, on some real data sets. We use Mapper for visualization of a data sets, and we tend to get some insights if some key characteristics of the data are captured by the visualization and how they are connected with human perception of the data. Also, we will discuss if the visualization can make progress in further work.