Local centrality information passing clustering algorithm for tags
- Publication Type:
- Journal Article
- Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 2013, 34 (4), pp. 499 - 504
- Issue Date:
|Local centrality information passing clustering algorithm for tags.caj||Published Version||185.25 kB|
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
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: