 |
In this paper, we have studied and evaluated the DPC algorithm. A range of experiments were conducted not only on read datasets but also on synthetic data in order to verify its performance. The obtained results show that DPC outperform K-means in terms of the Rand index, however, its advantages and disadvantages have also been depicted. To sum up, we highlight several issues that are limiting its practical application and propose some directions of further development of DPC as follows:
First, it is difficult for DPC in determining the cluster centers where the dataset is unbalanced overlapped or the data has clusters in which there are local minima.
Second, a combination with other algorithms to speed up the DPC algorithm can be considered, for example to reduce the time complexity of calculating the distance between points. The study to apply different types of distances for each dataset can also be further considered.
Finally, the research and development of semi-supervised clustering algorithms based on DPC is also a promising research direction. |
IEEE/ICACT20220302 Slide.14
[Big Slide]