| Title |
Comparison of clustering methods / |
| Translation of Title |
Klasterizacijos metodų palyginimas. |
| Authors |
Kilaire, Shanice |
| Full Text |
|
| Pages |
49 |
| Keywords [eng] |
The topic of this work is a comparison of 3 novel clustering methods that have been published recently and have not been compared before. |
| Abstract [eng] |
In this paper, a comparison of the three recently published novel clustering methods: Parameter free Clustering based K means, A Criterion for Deciding the Number of Clusters in a Dataset and Graph-based Data clustering via Multiscale Community Detection is undertaken. Each of the these methods are replicated on both real and synthetic data sets for which their performance is evaluated with the regards to the relative error between the number of outputted clusters and the true cluster number for each data set. It was found that the Graph-based Data clustering via Multiscale Community Detection was the best performing and was able to determine the cluster number for each data set with complete accuracy with regards to the number of partitions. Further evaluation of each method considers the quality of the output in more depth as well as evaluates the strengths and limitations of each respective method. |
| Dissertation Institution |
Vilniaus universitetas. |
| Type |
Master thesis |
| Language |
English |
| Publication date |
2024 |
