Mining a Web Citation Database for author co-citation analysis
Author co-citation analysis (ACA) has been widely used in bibliometrics as an analytical method in analyzing the intellectual structure of science studies. It can be used to identify authors from the same or similar research fields. However, such analysis method relies heavily on statistical tools to perform the analysis and requires human interpretation. Web Citation Database is a data warehouse used for storing citation indices of Web publications. In this paper, we propose a mining process to automate the ACA based on the Web Citation Database. The mining process uses agglomerative hierarchical clustering (AHC) as the mining technique for author clustering and multidimensional scaling (MDS) for displaying author cluster maps. The clustering results and author cluster map have been incorporated into a citation-based retrieval system known as PubSearch to support author retrieval of Web publications.
web citation database author co-citation analysis data mining intelligent information retrieval