Locally Weighted Ensemble Clustering

Abstract: Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering. It remains an open problem how to evaluate the reliability of clusters and exploit the local diversity in the ensemble to enhance the consensus performance, especially in the case when there is no access to data features or specific assumptions on data distribution. To address this, in this paper, we propose a novel ensemble clustering approach based on ensemble-driven cluster uncertainty estimation and local weighting strategy. In particular, the uncertainty of each cluster is estimated by considering the cluster labels in the entire ensemble via an entropic criterion. A novel ensemble-driven cluster validity measure is introduced, and a locally weighted co-association matrix is presented to serve as a summary for the ensemble of diverse clusters. With the local diversity in ensembles exploited, two novel consensus functions are further proposed. Extensive experiments on a variety of real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art.

• The paper introduces a novel method that computes cluster uncertainty using an entropic criterion to assess the reliability of base clusterings.
• It proposes an Ensemble-Driven Cluster Validity Index and a Locally Weighted Co-association Matrix to integrate local cluster diversity effectively.
• Experimental results on fifteen real-world datasets show enhanced NMI and ARI scores, affirming the method's robustness and accuracy.

Locally Weighted Ensemble Clustering: An Expert Overview

The paper "Locally Weighted Ensemble Clustering" by Huang, Wang, and Lai discusses an innovative approach within the domain of ensemble clustering, focusing on the integration of cluster uncertainty estimation and a local weighting strategy. This method addresses the limitations of existing ensemble clustering techniques, which usually treat base clusterings uniformly without considering their individual reliability.

Problem Context and Motivation

Ensemble clustering has emerged as a robust method for integrating multiple clustering solutions into a consensus result, potentially yielding more accurate and reliable outcomes. Nevertheless, conventional ensemble clustering techniques often overlook the variable quality and reliability of each base clustering. They neglect the local diversity within base clusterings, which can lead to suboptimal performance when faced with low-quality clustering inputs. This paper tackles the challenge of evaluating cluster reliability and exploiting local diversity without depending on data features or assumptions about data distribution.

Proposed Methodology

The authors present a novel approach that involves ensemble-driven cluster uncertainty estimation and a local weighting strategy. The core innovation lies in computing the uncertainty of each cluster using an entropic criterion, which effectively leverages the diversity of cluster labels across the entire ensemble. The key features of their methodology include:

1. Cluster Uncertainty Estimation: The uncertainty of clusters is measured considering the distribution of cluster labels throughout the ensemble. This step is crucial for determining the reliability of each cluster without requiring access to the original data features.
2. Ensemble-Driven Cluster Validity Index (ECI): This index is used to evaluate and weight clusters based on their computed uncertainty. ECI provides a nuanced reliability measure at the cluster level, enhancing the robustness and accuracy of the consensus clustering.
3. Locally Weighted Co-association Matrix (LWCA): By incorporating cluster-level validity into ensemble clustering processes, the LWCA matrix adjusts the traditional co-association matrix to reflect local reliability. This provides a comprehensive summary of diverse clusters in the ensemble.
4. Consensus Functions: The method introduces two consensus functions—Locally Weighted Evidence Accumulation (LWEA) and Locally Weighted Graph Partitioning (LWGP)—which utilize the locally weighted information to derive the consensus clustering.

Experimental Evaluation

In extensive experiments across fifteen real-world datasets, the proposed framework demonstrates superior performance compared to state-of-the-art techniques. This is evident in the higher NMI and ARI scores achieved, indicating improvements in clustering accuracy. The approach consistently produces robust results across a range of ensemble sizes, substantiating the validity of employing local weighting strategies in ensemble clustering.

Theoretical and Practical Implications

Theoretically, this research advances ensemble clustering by introducing an efficient means of incorporating cluster uncertainty without accessing data features or making distributional assumptions. Practically, the approach is applicable to a wide variety of datasets, particularly those where data features may be unavailable or unreliable, broadening its usage potential in diverse real-world scenarios.

Future Research Directions

This paper nudges future inquiries into further refining local weighting strategies or extending the framework to dynamic ensemble clustering environments. Additionally, exploring hybrid models that combine the strengths of the described method with other machine learning paradigms could be a fruitful area of research.

In conclusion, this work significantly contributes to ensemble clustering literature by addressing pressing challenges related to cluster reliability and diversity, setting a foundation for further exploration and application in more intricate data clustering problems.