Data Mining of Machine Learning Performance Data
Remzi Ibrahim · 2000
ith the development and penetration of data mining within different fields and industries, many data mining algorithms have emerged. The selection of a good data mining algorithm to obtain the best result on a particular data set has become very important. What works well for a particular data set may not work well on another. The goal of this thesis is to find associations between classification algorithms and characteristics of data sets by first building a file of data sets, their characteristics and the performance of a number of algorithms on each data set; and second applying unsupervised clustering analysis to this file to analyze the generated clusters and determine whether there are any significant patterns. Six classification algorithms were applied to 59 data sets and then three clustering algorithms were applied to the data generated. The patterns and properties of the clusters formed were then studied. The six classification algorithms used were OneR (1R), Kernel Density, ...