Hybrid Clustering of Text Mining and Bibliometrics Applied to Journal Sets.

To obtain correlated and complementary information contained in text mining and bibliometrics, hybrid clustering to incorporate textual content and citation information has become a popular strategy. In this paper, we propose a new computational framework of integrating text mining and bibliometrics to provide a mapping of journal sets. Two different approaches of hybrid clustering methods are applied in this paper. The first category is ensemble clustering, which combines different clustering results obtained from individual data into a consolidated clustering result. The second category is kernel fusion, which maps heterogeneous data sets into the kernel space and combines the kernel matrices for clustering. Kernels can be combined either averagely, or by an optimized weighted linear combination model. In this paper, we propose a novel adaptive kernel K-means clustering algorithm to combine textual content and citation information for clustering. The proposed algorithm is systemat...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Bart De Moor, Frizo A. L. Janssens, Shi Yu, Wolfgang Glänzel, Xinhai Liu, Yves Moreau
• Keywords: Computer Science, Hybrid Clustering Methods, Hybrid Clustering Strategy, K-means Clustering Algorithm, SDM 2009