Web proceedings papers


Petar Sekuloski and Vesna Dimitrievska Ristovska


Persistent Homology (PH) has emerged as a fundamental technique in the field of Topological Data Analysis (TDA) and has proven to be highly valuable in various domains of Machine Learning and Data Science in recent times. This methodology effectively captures the topological properties of datasets by integrating methods from algebraic topology, statistics, and computer science. In this paper we analyze and apply Persistence Homology as a tool for TDA, on some seismology dataset. We use PH for extracting the topological characteristics of a data set, and we tend to get some insights if some key characteristics of the data are captured by PH and how they are connected with human perception of the data.


Topological Data Analysis, Persistence Homology, Persistent diagram, Rips filtration