Appendix
A.1. Weighted Clustering Ensemble Algorithm Analysis
This appendix comprises empirical studies on the capabilities and limitations of our weighted clustering ensemble (WCE) algorithm based on the algorithm analysis described in Section 7.2.5.
As pointed out in Section 7.2.5, the Eq. (7.10) critically determines the performance of our WCE via the quantities |μm − wm|. As a result, we need both μm and wm for a given data set 
. While wm is achieved by applying a clustering validation criteria or a combination of them to the input partitions, μm is generally unavailable unless we know both the ground-truth partition and all possible ...
. While wm is achieved by applying a clustering validation criteria or a combination of them to the input partitions, μm is generally unavailable unless we know both the ground-truth partition and all possible ...Get Temporal Data Mining via Unsupervised Ensemble Learning now with the O’Reilly learning platform.
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