Study of models clustering and its application to ensemble learning

Clustering technique is an important tool for data analysis and has a promising prospect in data mining, pattern recognition, etc. Usually, objects in clustering analysis are of vectors, which consist of some features.. They may be represented as points in Euclidean space. However, in some tasks, objects in clustering analysis may be some abstract models other than data points, for example neural networks, decision trees, support vector machines, etc. By defining the extended distance (in real tasks, there are some different definition forms about distance), clustering method is studied for the abstract data objects. Framework of clustering algorithm for objects of models is presented As its application, a method for improving diversity of ensemble learning with neural networks is investigated. The relations between the number of clusters in clustering analysis, the size of ensemble learning, and performance of ensemble learning are studied by experiments.