Distributed K-Median Clustering with Application to Image Clustering

Developing algorithms suitable for distributed environments is important as data becomes more distributed. This paper proposes a distributed KMedian clustering algorithm for use in a distributed environment with centralized server, such as the Napster model in a peer-to-peer environment. Several approximate methods for computing the median in a distributed environment are proposed and analyzed in the context of the iterative K-Median algorithm. The proposed algorithm allows the clustering of multivariate data while ensuring that each cluster representative remains an item in the collection. This facilitates exploratory analysis where retaining a representative in the collection is important, such as imaging applications.