Graph Probability Aggregation Clustering

TL;DR

The paper introduces Graph Probability Aggregation Clustering (GPAC), a novel graph-based fuzzy clustering method that combines global and local clustering strengths, optimized via a multi-constrained approach for improved accuracy and scalability.

Contribution

GPAC unifies global and local clustering objectives into a multi-constrained optimization framework, incorporating probability aggregation and an acceleration method for large-scale data.

Findings

GPAC outperforms existing methods in clustering accuracy.

GPAC achieves linear computational complexity for large datasets.

GPAC demonstrates superior scalability and efficiency.

Abstract

Traditional clustering methods typically focus on either cluster-wise global clustering or point-wise local clustering to reveal the intrinsic structures in unlabeled data. Global clustering optimizes an objective function to explore the relationships between clusters, but this approach may inevitably lead to coarse partition. In contrast, local clustering heuristically groups data based on detailed point relationships, but it tends to be less coherence and efficient. To bridge the gap between these two concepts and utilize the strengths of both, we propose Graph Probability Aggregation Clustering (GPAC), a graph-based fuzzy clustering algorithm. GPAC unifies the global clustering objective function with a local clustering constraint. The entire GPAC framework is formulated as a multi-constrained optimization problem, which can be solved using the Lagrangian method. Through the optimization process, the probability of a sample belonging to a specific cluster is iteratively calculated by aggregating information from neighboring samples within the graph. We incorporate a hard assignment variable into the objective function to further improve the convergence and stability of optimization. Furthermore, to efficiently handle large-scale datasets, we introduce an acceleration program that reduces the computational complexity from quadratic to linear, ensuring scalability. Extensive experiments conducted on synthetic, real-world, and deep learning datasets demonstrate that GPAC not only exceeds existing state-of-the-art methods in clustering performance but also excels in computational efficiency, making it a powerful tool for complex clustering challenges.