Improved Generality-based Concept Formation Clustering Algorithm
Li Gan · Coal Technology · 2011
Generality-based Concept Formation(GCF) is a type of hierarchical clustering algorithm for categorical(symbolic) data.Improvements on GCF have been made in two aspects as follows.Firstly,a novel categorical similarity metric based on conditional probability distribution is present and used in the improved algorithm.Not only can the similarity metric measure the difference in the numbers of dissimilar attributes between two clusters,but also the key lies in expressing the different degrees of them,whose nature is to do numerical processing of categorical data.Secondly,this paper suggests the notion of similarity quality and designs its calculation formula.In the improved algorithm,the similarity quality is utilized with the sample variation coefficient at the same time so as to render the generality level used in the clustering process to be more adaptive.Theoretical analysis and experimental results indicate that the improved GCF algorithm is valid.