Hybrid Clustering by Integrating Text and Citation Based Graphs in Journal Database Analysis

We propose a hybrid clustering strategy by integrating heterogeneous information sources as graphs. The hybrid clustering method is extended on the basis of modularity based Louvain method. We introduce two different approaches, graph coupling and graph fusion. The weights of these combined graphs are optimized with the criterion of maximizing the Average Normalized Mutual Information (ANMI). The methods are applied to obtain structural mapping of large scale Web of Science (WoS) journal database by integrating attribute based textual information and relation based citation information. From the experimental, the proposed graph combination scheme is compared with individual graph clustering, spectral clustering and Vector Space Model (VSM) based clustering methods.