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Data Mining : in der Medizin und Medizintechnik /Mikut, Ralf. January 2008 (has links)
Teilw. zugl.: Karlsruhe, Univ., Habil.-Schr., 2007 u.d.T.: Mikut, Ralf: Automatisierte Datenanalyse in der Medizin und Medizintechnik.
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Meta-learning: strategies, implementations, and evaluations for algorithm selection /Köpf, Christian Rudolf. January 2006 (has links)
Univ., Diss.--Ulm, 2005. / Literaturverz. S. 227 - 248.
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Component based user guidance in knowledge discovery and data mining /Engels, Robert. January 1999 (has links)
Thesis (doctoral)--Universität, Karlsruhe, 1999.
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An optimization algorithm for clustering using weighted dissimilarity measuresChan, Yat-ling., 陳逸靈. January 2003 (has links)
published_or_final_version / abstract / toc / Mathematics / Master / Master of Philosophy
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Mining frequent itemsets and order preserving submatrices from uncertain dataChui, Chun-kit, 崔俊傑 January 2007 (has links)
published_or_final_version / abstract / Computer Science / Master / Master of Philosophy
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Security in association rule miningWong, Wai-kit, 王偉傑 January 2007 (has links)
published_or_final_version / abstract / Computer Science / Master / Master of Philosophy
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Text subspace clustering with feature weighting and ontologiesJing, Liping., 景麗萍. January 2007 (has links)
published_or_final_version / abstract / Mathematics / Doctoral / Doctor of Philosophy
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The complexities of tracking quantiles and frequent items in a data streamHung, Yee-shing, Regant., 洪宜成. January 2009 (has links)
published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy
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Knowledge discovery from distributed aggregate data in data warehouses and statistical databasesPaÌirceÌir, RoÌnaÌn January 2002 (has links)
No description available.
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Geo-demographic analysis in support of the United States Army Reserve (USAR) Unit Positioning and Quality Assessment Model (UPQUAM)Fair, Martin Lynn 06 1900 (has links)
Manning United States Army Reserve (USAR) units are fundamentally different from manning Regular Army (RA) units. A soldier assigned to a USAR unit must live within 75 miles or 90 minutes commute of his Reserve Center (RC). This makes reserve unit positioning a key factor in the ability to recruit to fill the unit. This thesis automates, documents, reconciles, and assembles data on over 30,000 ZIP Codes, over 800 RCs, and over 260 Military Occupational Specialties (MOSs), drawing on and integrating over a dozen disparate databases. This effort produces a single data file with demographic, vocational, and economic data on every ZIP Code in America, along with the six year results of its RA, USAR, sister service recruit production, and MOS suitability for each of the 264 MOSs. Preliminary model development accounts for about 70% recruit production variation by ZIP Code. This thesis also develops models for the top five MOSs to predict the maximum number of recruits obtained from a ZIP Code for that MOS. Examples illustrate that ZIP Codes vary in their ability to provide recruits with sufficient aptitude for technical fields. Two subsequent theses will use those results. One completes the MOS models. The second uses the models as constraints in an optimization model to position RCs. An initial version of the optimization model is developed in this thesis. Together, the three theses will provide a powerful tool for analysis of a strategic-based optimal reserve force stationing. / Lieutenant Colonel, United States Army
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