Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Automatic categorization of query results
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Getting our head in the clouds: toward evaluation studies of tagclouds
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tag clouds for summarizing web search results
Proceedings of the 16th international conference on World Wide Web
A tutorial on spectral clustering
Statistics and Computing
The folksonomy tag cloud: when is it useful?
Journal of Information Science
Seeing things in the clouds: the effect of visual features on tag cloud selections
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Synchronized tag clouds for exploring semi-structured clinical trial data
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
CourseCloud: summarizing and refining keyword searches over structured data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Semantically structured tag clouds: an empirical evaluation of clustered presentation approaches
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Phrase-based hierarchical clustering of web search results
ECIR'03 Proceedings of the 25th European conference on IR research
Using tag clouds to promote community awareness in research environments
CDVE'10 Proceedings of the 7th international conference on Cooperative design, visualization, and engineering
On the selection of tags for tag clouds
Proceedings of the fourth ACM international conference on Web search and data mining
Methodologies for improved tag cloud generation with clustering
ICWE'12 Proceedings of the 12th international conference on Web Engineering
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Tag clouds are means for navigation and exploration of information resources on the web provided by social Web sites. The most used approach to generate a tag cloud so far is based on popularity of tags among users who annotate by those tags. This approach however has several limitations, such as suppressing number of tags which are not used often but could lead to interesting resources as well as tags which have been suppressed due to the default number of tags to present in the tag cloud. In this paper we propose the SimSpectrum: a similarity based spectral clustering approach to generate a tag cloud which improves the current state of the art with respect to these limitations. Our approach is based on finding to which extent the tags are related by a similarity calculus. Based on the results from similarity calculation, the spectral clustering algorithm finds the clusters of tags which are strongly related and are loosely related to the other tags. By doing so, we can cover part of the tags which are discarded by traditional tag cloud generation approaches and therefore, present the user with more opportunities to find related interesting web resources. We also show that in terms of the metrics that capture the structural properties of a tag cloud such as coverage and relevance our method has significant results compared to the baseline tag cloud that relies on tag popularity. In terms of the overlap measure, our method shows improvements against the baseline approach. The proposed approach is evaluated using MedWorm medical article collection.