Hybrid clustering of multi-view data via Tucker-2 model and its application

Xinhai Liu (), Wolfgang Glänzel and Bart De Moor
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Xinhai Liu: Wuhan University of Science and Technology
Wolfgang Glänzel: Katholieke Universiteit Leuven
Bart De Moor: Katholieke Universiteit Leuven

Scientometrics, 2011, vol. 88, issue 3, No 9, 819-839

Abstract: Abstract With the modern technology fast developing, most of entities can be observed by different perspectives. These multiple view information allows us to find a better pattern as long as we integrate them in an appropriate way. So clustering by integrating multi-view representations that describe the same class of entities has become a crucial issue for knowledge discovering. We integrate multi-view data by a tensor model and present a hybrid clustering method based on Tucker-2 model, which can be regarded as an extension of spectral clustering. We apply our hybrid clustering method to scientific publication analysis by integrating citation-link and lexical content. Clustering experiments are conducted on a large-scale journal set retrieved from the Web of Science (WoS) database. Several relevant hybrid clustering methods are cross compared with our method. The analysis of clustering results demonstrate the effectiveness of the proposed algorithm. Furthermore, we provide a cognitive analysis of the clustering results as well as the visualization as a mapping of the journal set.

Keywords: Hybrid clustering; Multi-view data; Text mining; Bibliometric analysis (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s11192-011-0348-3

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