Visual Quality Assessment of Subspace Clusterings
نویسندگان
چکیده
The quality assessment of results of clustering algorithms is challenging as different cluster methodologies lead to different cluster characteristics and topologies. A further complication is that in high-dimensional data, subspace clustering adds to the complexity by detecting clusters in multiple different lower-dimensional projections. The quality assessment for (subspace) clustering is especially difficult if no benchmark data is available to compare the clustering results. In this research paper, we present SubEval, a novel subspace evaluation framework, which provides visual support for comparing quality criteria of subspace clusterings. We identify important aspects for evaluation of subspace clustering results and show how our system helps to derive quality assessments. SubEval allows assessing subspace cluster quality at three different granularity levels: (1) A global overview of similarity of clusters and estimated redundancy in cluster members and subspace dimensions. (2) A view of
منابع مشابه
Title in English: Methods for Comparing Subspace Clusterings
of Licentiate's thesis Abstract: Subspace clustering methods aim to find groups of similar data points in various subspaces of the original data space. They combine and generalize clustering and feature extraction. Subspace clustering methods are becoming more and more popular , and new algorithms are being published at an increasing rate. These algorithms have been successfully applied for ins...
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