Introduction
Many real world applications have datasets consisting of high dimensional feature spaces. For example, the gene expression data record the expression levels of a set of thousands of genes under hundreds of experimental conditions. Traditional clustering algorithms fail to efficiently find clusters of genes that demonstrate similar expression levels in all conditions due to such a high dimensional feature space. Subspace clustering addresses this problem by looking for patterns in subspaces [1] instead of in the full dimensional space. A lot of work has been done in developing efficient subspace clustering algorithms for datasets of various characteristics [1, 6].
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Bian, H., Bhatnagar, R. (2009). Mining Subspace Clusters from Distributed Data. In: Lee, R., Hu, G., Miao, H. (eds) Computer and Information Science 2009. Studies in Computational Intelligence, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01209-9_7
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