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dc.contributor.advisorGilbert, Juan
dc.contributor.advisorNarayanan, N. Harien_US
dc.contributor.advisorSeals, Cherylen_US
dc.contributor.authorWilliams, Philicityen_US
dc.date.accessioned2009-02-23T15:56:27Z
dc.date.available2009-02-23T15:56:27Z
dc.date.issued2008-08-15en_US
dc.identifier.urihttp://hdl.handle.net/10415/1546
dc.description.abstractToday’s data storage and collection abilities have allowed the accumulation of enormous amounts of data. Data mining can be a useful tool in transforming these large amounts of raw data into useful information. Predictive modeling is a very popular area in data mining. The results of these type tasks can contain helpful information that can be used in decision making. Ensemble method techniques involve using the results of multiple models in combination. Research has shown that by applying an ensemble method approach to predictive modeling one can increase the model’s accuracy. However, these techniques focus on classification data mining algorithms. This research investigates the notion of using a data clustering and predictive modeling approach to increase predictive model accuracy.en_US
dc.language.isoen_USen_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectComputer Science and Software Engineeringen_US
dc.titleClustering and Predictive Modeling: An Ensemble Approachen_US
dc.typeThesisen_US
dc.embargo.lengthMONTHS_WITHHELD:36en_US
dc.embargo.statusEMBARGOEDen_US
dc.embargo.enddate2012-02-23en_US


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