Staff

Doctoral studies

Theme Clustering algorithms for medical big data

Supervisor prof. dr. Audronė Jakaitienė

Study time 2017 10 01–2021 09 30

Aim: The aim is to propose a clustering algorithm that would increase the performance of clustering on medical big data.

Main problems:

1. To investigate most popular clustering methods commonly used for medical big data clustering.
2. To compare selected clustering algorithms using real medical data sets.
3. To propose a new clustering algorithm to improve the performance of clustering on medical big data.
4. To propose a solution for the implementation of proposed clustering algorithm in real-world medical data system.