Date of Graduation
2007
Document Type
Thesis
Degree Type
MS
Committee Chair
Tim Menzies
Abstract
Data discretization is a commonly used preprocessing method in data mining. Several authors have put forth claims that a particular method they have written performs better than other competing methods in this field. Examining these methods we have found that they rely upon unnecessarily complex data structures and techniques in order to perform their preprocessing. They also typically involve sorting each new record to determine its location in the preceding data. We describe what we consider to be a simple discretization method based upon a randomized binary search tree that provides the sorting routine as one of the properties of inserting into the data structure. We then provide an experimental design to compare our simple discretization method against common methods used prior to learning with Na¨ıve Bayes Classifiers. We find very little variation between the performance of commonly used methods for discretization. Our findings lead us to believe that while there is no single best method of discretization for Na¨ıve Bayes Classifiers, simple methods perform as well or nearly as well as complex methods and are thus viable methods for future use.
Recommended Citation
Boland, Donald Joseph Jr., "Data discretization simplified: Randomized binary search trees for data preprocessing." (2007). Graduate Theses, Dissertations, and Problem Reports (ETD). 11020.
https://researchrepository.wvu.edu/etd/11020