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The measure of data reliability has recently proven useful for a number of data analysis tasks. This paper extends the underlying metric to a new problem of soft subspace clustering. The concept of subspace clustering has been increasingly recognized as an effective alternative to conventional algorithms (which search for clusters without differentiating the significance of different data attributes). While a large number of crisp subspace approaches have been proposed, only a handful of soft counterparts are developed with the common goal of acquiring the optimal cluster-specific dimension weights. Most soft subspace clustering methods work based on the exploitation of k-means and greatly rely on the iteratively disclosed cluster centers for the determination of local weights. Unlike such wrapper techniques, this paper presents a filter approach which is efficient and generally applicable to different types of clustering. Systematical experimental evaluations have been carried out over a collection of published gene expression data sets. The results demonstrate that the reliability-based methods generally enhance their corresponding baseline models and outperform several well-known subspace clustering algorithms.