XML Clustering Based on Common Neighbor

Abstract

Clustering on XML documents is an important task. However, it is difficult to select the appropriate parameters’ value for the clustering algorithms. By integrating outlier detection with clustering, the paper takes a new approach for analyzing the XML documents by structure distance. After stating the XML tree distance, the paper proposes a new clustering algorithm, which stops clustering automatically by utilizing the outlier information and needs only one parameter, whose appropriate value range can be decided in the outlier mining process. The paper adopts the XML dataset with different structure and other real-life datasets to compare it with other clustering algorithms.