Reviews: K-Medoids For K-Means Seeding

The authors propose to use a particular version of the K-medoids algorithm (clarans - that uses iterative swaps to identify the medoids) for initializing k-means and claim that this improves the final clustering quality. The authors have also tested their claims with multiple datasets, and demonstrated their performance improvements. They have also published code that will be made open after the review process. The paper is easy to read and follow, and the authors have done a good job placing their work in context. I appreciate the fact that the optimizations are presented in a very accessible manner in Section 4. As the authors claim, open source code is an important contribution.