A Link-Based Approach to the Cluster Ensemble Problem

Abstract

Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information of a cluster ensemble. The underlying ensemble-information matrix reflects only cluster-data point relations, while those among clusters are generally overlooked. This paper presents a new link-based approach to improve the conventional matrix. It achieves this using the similarity between clusters that are estimated from a link network model of the ensemble. In particular, three new link-based algorithms are proposed for the underlying similarity assessment. The final clustering result is generated from the refined matrix using two different consensus functions of feature-based and graph-based partitioning. This approach is the first to address and explicitly employ the relationship between input partitions, which has not been emphasized by recent studies of matrix refinement. The effectiveness of the link-based approach is empirically demonstrated over 10 data sets (synthetic and real) and three benchmark evaluation measures. The results suggest the new approach is able to efficiently extract information embedded in the input clusterings, and regularly illustrate higher clustering quality in comparison to several state-of-the-art techniques.

Clustering ensemble method

“…The final partition, 'meta-clustering', is obtained using METIS [24]. [20] aimed to redefine the Co-association matrix to also take into account the relationship between clusters estimated from a link network model, and to interpret this matrix as feature vectors or a Bipartite graph. It used an ordinary clustering algorithm onto the new similarity matrix in order to generate the final cluster result.…”

Locally Weighted Ensemble Clustering

“…In the past decade, many ensemble clustering approaches have been developed, which can be mainly classified into three categories, i.e., the pair-wise co-occurrence based approaches [20], [22], [23], [24], [29], [31], the graph partitioning based approaches [17], [19], [30], [28], and the median partition based approaches [18], [21], [25], [32].…”

“…These algorithms attempt to solve the ensemble clustering problem in various ways [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [26], [31], [32]. However, one common limitation to most of the existing methods is that they generally treat all clusters and all base clusterings in the ensemble equally and may suffer from low-quality clusters or low-quality base clusterings.…”

“…Zhong et al [39] exploited the Euclidean distances between objects to estimate the cluster reliability, which needs access to the original data features and is only applicable to numerical data. However, in the more general formulation of ensemble clustering [17], [18], [19], [20], [21], [22], [23], [24], [25], [32], the original data features are not available in the consensus process. Moreover, by measuring the within-cluster similarity based on Euclidean distances, the efficacy of the method in [39] heavily relies on some implicit assumptions about data distribution, which places an unstable factor in the consensus process.…”

A new link-based method to ensemble clustering and cancer microarray data analysis

“…It has proven effective for many problem domains, especially microarray data analysis. Among different state-of-the-art methods, the link-based approach (LCE) recently introduced by Iam-On et al (2011) provides a highly accurate clustering. This paper presents the improvement of LCE with a new link-based metric being developed and engaged.…”