Local centrality information passing clustering algorithm for tags

Publication Type:
Journal Article
Citation:
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 2013, 34 (4), pp. 499 - 504
Issue Date:
2013-04-01
Full metadata record
Files in This Item:
Filename Description Size
Local centrality information passing clustering algorithm for tags.cajPublished Version185.25 kB
Unknown
In recent years, most of the proposed tag clustering algorithms directly deal with the tag data by using traditional clustering algorithms, such as: K-means or Single-linkage devices. Nevertheless, the inherent drawbacks of using these traditional clustering algorithms have badly influenced the quality of tag clustering. In this paper, a clustering algorithm named local centrality information passing clustering (LCIPC) was proposed in an attempt to find out how to achieve high quality tag clustering results. First, we utilized the LCIPC, to construct a KNN directed neighbor graph G based on the similarities of the tag's; secondly, the local centrality value of every tag was calculated by using a KNN kernel density estimator; and thirdly, the local centrality value was passed onto the graph G by running a random walk method to generate global centrality rank listings. Eventually, tag clustering results were created based on global centrality ranking list by using an in-depth search first algorithm. The experimental results conducted utilizing three real world datasets indicate that the proposed LCIPC method has the ability of finding high quality tag clustering results.
Please use this identifier to cite or link to this item: