Spectral Active Clustering via Purification of the k-Nearest Neighbor Graph

Spectral clustering is widely used in data mining, machine learning and pattern recognition. There have been some recent developments in adding pairwise constraints as side information to enforce top-down structure into the clustering results. However, most of these algorithms are “passive” in the sense that the side information is provided beforehand. In this paper, we present a spectral active clustering method that actively select pairwise constraints based on a novel notion of node uncertainty rather than pair uncertainty. In our approach, the constraints are used to drive a purification process on the k-nearest neighbor graph—edges are removed from the graph based on the constraints—that ultimately leads to an improved, constraint-satisfied clustering. We have evaluated our framework on three datasets (UCI, gene and image sets) in the context of baseline and state of the art methods and find the proposed algorithm to be superiorly effective.