Name
Abstract | ||
|---|---|---|
Tagging behavior on the Internet has seen dramatic increase in recent years, and social tagging has become a popular way to
organize and share resources. However, ambiguity and large quantities of tags restrict its effective use for resource searching
and classifying. Tag clustering can group tags with similar semantics together, thus helping alleviate these problems. In
this paper, we introduce a random walk-based method to measure relevance between tags by exploiting the relationship between tags and resources. Based on this,
we also develop a novel clustering method, TagClus, which can address several challenges in tag clustering. Experimental results on a real dataset show that our methods achieve
good accuracy and acceptable performance for tag clustering. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s10115-010-0307-y | Knowledge and Information Systems |
Keywords | Field | DocType |
tagging behavior,large quantity,tag clustering,dramatic increase,acceptable performance,good accuracy,random walk-based method,real dataset show,effective use,random walk | Data mining,Social network,Computer science,Random walk,Artificial intelligence,Cluster analysis,Ambiguity,The Internet,Metadata,Information retrieval,Parsing,Machine learning,Semantics | Journal |
Volume | Issue | ISSN |
27 | 2 | 0219-3116 |
Citations | PageRank | References |
13 | 0.53 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianwei Cui | 1 | 27 | 3.37 |
| Hongyan Liu | 2 | 517 | 46.49 |
| Jun He | 3 | 230 | 19.86 |
| Pei Li | 4 | 81 | 4.79 |
| Xiaoyong Du | 5 | 882 | 123.29 |
| Puwei Wang | 6 | 74 | 8.00 |