问题
I have researched that K-medoid Algorithm (PAM) is a parition-based clustering algorithm and a variant of K-means algorithm. It has solved the problems of K-means like producing empty clusters and the sensitivity to outliers/noise.
However, the time complexity of K-medoid is O(n^2), unlike K-means (Lloyd's Algorithm) which has a time complexity of O(n). I would like to ask if there are other drawbacks of K-medoid algorithm aside from its time complexity.
回答1:
The main disadvantage of K-Medoid algorithms (either PAM, CLARA or CLARANS) is that they are not suitable for clustering non-spherical (arbitrary shaped) groups of objects. This is because they rely on minimizing the distances between the non-medoid objects and the medoid (the cluster center) - briefly, they use compactness as clustering criteria instead of connectivity.
Another disadvantage of PAM is that it may obtain different results for different runs on the same dataset because the first k medoids are chosen randomly.
In addition to the aforementioned disadvantages, you must also specify the value for k (the number of clusters) in advance.
来源：https://stackoverflow.com/questions/46514123/drawbacks-of-k-medoid-pam-algorithm