Staff

Doctoral studies

Theme Clustering Algorithms for Medical Big Data

Supervisor Prof. Dr. Audronė Jakaitienė

Study time 2017 10 01–2023 09 16 (2019 10 16–2021 09 30 academic holidays)

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.