| Evaluating cluster preservation in frequent itemset integration for distributed databases. | |
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MedLine Citation:
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PMID: 20703684 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Medical sciences are rapidly emerging as a data rich discipline where the amount of databases and their dimensionality increases exponentially with time. Data integration algorithms often rely upon discovering embedded, useful, and novel relationships between feature attributes that describe the data. Such algorithms require data integration prior to knowledge discovery, which can lack the timeliness, scalability, robustness, and reliability of discovered knowledge. Knowledge integration algorithms offer pattern discovery on segmented and distributed databases but require sophisticated methods for pattern merging and evaluating integration quality. We propose a unique computational framework for discovering and integrating frequent sets of features from distributed databases and then exploiting them for unsupervised learning from the integrated space. Assorted indices of cluster quality are used to assess the accuracy of knowledge merging. The approach preserves significant cluster quality under various cluster distributions and noise conditions. Exhaustive experimentation is performed to further evaluate the scalability and robustness of the proposed methodology. |
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Authors:
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Sumeet Dua; Michael P Dessauer; Prerna Sethi |
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Publication Detail:
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Type: Journal Article Date: 2010-05-09 |
Journal Detail:
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Title: Journal of medical systems Volume: 35 ISSN: 0148-5598 ISO Abbreviation: J Med Syst Publication Date: 2011 Oct |
Date Detail:
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Created Date: 2011-12-14 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 7806056 Medline TA: J Med Syst Country: United States |
Other Details:
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Languages: eng Pagination: 845-53 Citation Subset: IM |
Affiliation:
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Data Mining Research Laboratory, Department of Computer Science, Louisiana Tech University, Ruston, LA, 71272, USA, Sdua@latech.edu. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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