Comparison of Stability of Classical Taxonomy Bagging Metod with Bagging Based on Co-Occurence Data

Abstract

Ensemble approach has been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. Recently, analogous techniques for cluster analysis have been suggested in order to increase classification accuracy, robustness and stability of the clustering solutions. Research has proved that, by combining a collection of different clusterings, an improved solution can be obtained. The stability of a clustering algorithm with respect to small perturbations of data (e.g., data subsampling or small variations in the feature values) or the parameters of the algorithm (e.g., random initialization) is a desirable quality of the algorithm. On the other hand, ensembles benefit from diverse clusterers. Although built upon unstable components, the ensemble is expected to be more accurate and robust than the individual clustering method. Here, we look at the stability of the ensemble methods based on bagging idea and co-occurrence matrix. This paper carries out an experimental study to compare stability of bagging method used to the classical data set with bagging based on co-occurrence matrix.