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- W56661706 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2009 SIAM International Conference on Data Mining (SDM)Identifying Information-Rich Subspace Trends in High-Dimensional DataSnehal Pokharkar and Chandan K. ReddySnehal PokharkarDepartment of Computer Science at Wayne State University.Search for more papers by this author and Chandan K. ReddyDepartment of Computer Science at Wayne State University.Search for more papers by this authorpp.557 - 568Chapter DOI:https://doi.org/10.1137/1.9781611972795.48PDFBibTexSections ToolsAdd to favoritesDownload CitationsTrack CitationsEmail SectionsAboutAbstract Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in many science and engineering applications. The information quotient of large-scale high-dimensional datasets is significantly reduced by the curse of dimensionality which makes the traditional clustering and association analysis methods unsuitable. Most interesting patterns cannot be revealed using global methods which consider the entire data and feature spaces during their analysis. Identifying some interesting patterns in large scale high-dimensional data is usually accomplished using popular techniques such as dimensionality reduction, feature selection and subspace clustering. Though these methods are successfully able to identify the groupings in the feature subsets and localized neighborhood data subspaces, none of these methods extract the latent patterns that are present in local information-rich subsets of the data. In this paper, we seek an information-revealing representation of the data subsets and features that may contain local patterns. We formalize the problem of identifying ‘subspace trends’ in high-dimensional datasets focusing on information-rich subsets and develop a new algorithm to extract such subspace trends. We demonstrate our results on both synthetic and real-world datasets and show the superiority of the proposed methodology over traditional clustering and dimensionality reduction techniques. Previous chapter Next chapter RelatedDetails Published:2009ISBN:978-0-89871-682-5eISBN:978-1-61197-279-5 https://doi.org/10.1137/1.9781611972795Book Series Name:ProceedingsBook Code:PR133Book Pages:1-1244Key words:Clustering, subspaces, dimensionality reduction, trend analysis, regression, feature selection" @default.
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- W56661706 title "Identifying Information-Rich Subspace Trends in High-Dimensional Data" @default.
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