SCE: A manifold regularized set-covering method for data partitioning
This is an incremental improvement for data analysis practitioners, addressing a specific limitation in cluster ensemble techniques.
The paper tackled the problem of cluster ensemble methods neglecting raw data structure by proposing a Structural Cluster Ensemble (SCE) algorithm that incorporates manifold regularization and discriminative constraints, showing effectiveness on synthetic and real datasets.
Cluster analysis plays a very important role in data analysis. In these years, cluster ensemble, as a cluster analysis tool, has drawn much attention for its robustness, stability, and accuracy. Many efforts have been done to combine different initial clustering results into a single clustering solution with better performance. However, they neglect the structure information of the raw data in performing the cluster ensemble. In this paper, we propose a Structural Cluster Ensemble (SCE) algorithm for data partitioning formulated as a set-covering problem. In particular, we construct a Laplacian regularized objective function to capture the structure information among clusters. Moreover, considering the importance of the discriminative information underlying in the initial clustering results, we add a discriminative constraint into our proposed objective function. Finally, we verify the performance of the SCE algorithm on both synthetic and real data sets. The experimental results show the effectiveness of our proposed method SCE algorithm.