# SCE: A manifold regularized set-covering method for data partitioning

## TL;DR

This paper introduces SCE, a novel manifold regularized set-covering method for data partitioning that incorporates data structure and discriminative information to improve clustering robustness and accuracy.

## Contribution

The paper proposes a new cluster ensemble algorithm that integrates structure and discriminative information via a Laplacian regularized set-covering formulation.

## Key findings

- SCE outperforms traditional ensemble methods on synthetic data.
- SCE achieves higher clustering accuracy on real datasets.
- The method effectively captures data structure and discriminative features.

## Abstract

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.