Clustering high dimensional data and identifying central nodes in a graph are complex and computationally expensive tasks. We utilize k-nn graph of high dimensional data as efficient representation of the hidden structure of the clustering problem. Initial cluster centers are determined by graph centrality measures. Cluster centers are fine-tuned by minimizing fuzzy-weighted geodesic distances. The shortest-path based representation is parallel to the concept of transitive closure. Therefore, our algorithm is capable to cluster networks or even more complex and abstract objects based on their partially known pairwise similarities. The algorithm is proven to be effective to identify senior researchers in a co-author network, central cities in topographical data, and clusters of documents represented by high dimensional feature vectors.
ASJC Scopus subject areas
- Artificial Intelligence