An implementation of the relational k-means algorithm
This work provides a tool for clustering in domains where Euclidean assumptions do not hold, but it is incremental as it focuses on implementation rather than new algorithmic development.
The paper presents a C# implementation of relational k-means, a generalization of k-means clustering that handles non-Euclidean scenarios by using an arbitrary distance matrix as input, enabling clustering of objects not represented as vectors.
A C# implementation of a generalized k-means variant called relational k-means is described here. Relational k-means is a generalization of the well-known k-means clustering method which works for non-Euclidean scenarios as well. The input is an arbitrary distance matrix, as opposed to the traditional k-means method, where the clustered objects need to be identified with vectors.