Show simple item record

dc.contributor.advisorAcuña, Edgar
dc.contributor.authorCoaquira Nina, Frida R.
dc.date.accessioned2019-02-12T15:30:46Z
dc.date.available2019-02-12T15:30:46Z
dc.date.issued2007
dc.identifier.urihttps://hdl.handle.net/20.500.11801/1791
dc.description.abstractKnowledge Discovery in Databases (KDD) is the nontrivial extraction of implicit, previously unknown and potentially useful information from data. Data preprocessing is a step of the KDD process that reduces the complexity of the data and offers better conditions to subsequent analysis. Rough sets theory, where sets are approximated using elementary sets, is another approach for developing methods for the KDD process. In this doctoral Thesis, we propose new algorithms based on Rough sets theory for three data preprocessing steps: Discretization, feature selection, and instance selection. In Discretization, continuous features are transformed into new categorical features. This is required for some KDD algorithms working strictly with categorical features. In Feature selection, the new subset of features leads to a new dataset of lower dimension, where it is easier to perform a KDD task. When a dataset is very large, an instance selection process is required to decrease the computational complexity of the KDD process. In addition to that, we combine a partitioning clustering algorithm with the Rough sets approach obtaining comparable results to a hierarchical clustering algorithm used along with rough sets. The new methods proposed in this thesis have been tested on datasets taken from the Machine Learning Database Repository at the University of California at Irvine.en_US
dc.language.isoEnglishen_US
dc.subjectRough set theoryen_US
dc.subjectKnowledge discoveryen_US
dc.titleOn applications of rough sets theory to knowledge discoveryen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c) 2007 Frida R. Coaquira Ninaen_US
dc.contributor.committeeMacchiavelli, Raul
dc.contributor.committeeSaito, Tokuji
dc.contributor.committeeVega, Fernando
dc.contributor.representativeCordova, Mario
thesis.degree.levelPh.D.en_US
thesis.degree.disciplineComputing and Information Sciences and Engineeringen_US
dc.type.thesisDissertationen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.description.graduationYear2007en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Theses & Dissertations
    Items included under this collection are theses, dissertations, and project reports submitted as a requirement for completing a degree at UPR-Mayagüez.

Show simple item record