Adaptive Affinity Propagation Clustering (0805.1096v1)

Abstract: Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter 'preference' can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clustering solution, adaptive adjustment of damping factors to eliminate oscillations, and adaptive escaping from oscillations when the damping adjustment technique fails. Experimental results on simulated and real data sets show that the adaptive AP is effective and can outperform AP in quality of clustering results.

• The paper introduces Adaptive Affinity Propagation (adAP), an enhanced clustering method addressing traditional AP's limitations in parameter selection and convergence oscillations.
• adAP incorporates adaptive mechanisms like preference scanning, damping, and escape to automatically determine the optimal number of clusters and ensure reliable convergence.
• Experimental results demonstrate that adAP consistently identifies the correct number of clusters, achieves higher clustering quality, and eliminates oscillations compared to standard AP.

Overview of Adaptive Affinity Propagation Clustering

The paper "Adaptive Affinity Propagation Clustering" presents a significant advancement in the area of clustering algorithms, specifically improving the traditional Affinity Propagation (AP) method. The authors critically address two major limitations of the existing AP algorithm: the selection of the 'preference' parameter and the issue of oscillations during convergence.

Key Innovations

The proposed Adaptive Affinity Propagation (adAP) method introduces several adaptive mechanisms to overcome these challenges:

1. Adaptive Preference Scanning: This approach dynamically searches the parameter space to determine the optimal 'preference' value, which directly influences the number of clusters (NC). By enabling an automatic exploration of clustering solutions across different NCs, this technique aims to yield a more effective clustering structure fitting the data's inherent properties.
2. Adaptive Damping: Oscillations, a recurring issue that affects convergence in AP, are mitigated using an adaptive damping factor. The adaptive damping incrementally increases the damping factor to suppress oscillations while maintaining the algorithm's speed.
3. Adaptive Escape: When the damping adjustment does not adequately resolve oscillations (potentially when the damping factor exceeds 0.85), an alternative strategy decreases the 'preference' parameter. This method attempts to reposition the solution path away from oscillatory regions.
4. Cluster Validation Technique: The adAP utilizes an internal cluster validity index, such as the Silhouette index, to assess and select the most optimal clustering configuration from the multiple solutions generated through adaptive preference scanning.

Experimental Evaluation

Extensive experimentation on simulated and real datasets, including gene expression data, showcases the effectiveness of the adAP. Notably, it was observed that:

• adAP consistently attained the correct number of clusters across diverse datasets, unlike the standard AP, which frequently led to suboptimal clustering results due to oscillations.
• The Silhouette index affirmed the superiority of the clustering quality in adAP solutions compared to AP as indicated by higher Fowlkes-Mallows index (FM) values for known datasets.
• Adaptive mechanisms in adAP automatically eliminated oscillations, thereby enhancing convergence reliability.

Implications and Future Directions

The adaptive enhancements proposed for AP extend its applicability to a broader range of clustering tasks. By automating crucial components that traditionally required manual tuning, adAP significantly eases the burden on users and allows for more robust performance across varying data types.

Future research could explore the integration of other cluster validity measures to further bolster adAP's ability to handle complex and overlapping cluster structures. Additionally, expanding the experimental evaluations to include more contemporary datasets with higher dimensionality and noise levels could give further insights into the algorithm's performance under challenging conditions.

In conclusion, the adaptive practices embedded in adAP represent a substantial improvement over the traditional AP algorithm, addressing key limitations while providing a scalable and efficient solution for clustering applications. These advancements open the door to more refined clustering capabilities in both theoretical explorations and practical deployments across diverse domains.