Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Readings in information visualization
Visualization of search results: a comparative evaluation of text, 2D, and 3D interfaces
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track1 - Volume 1
Mining Multidimensional Data Using Clustering Techniques
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
An interaction framework for mobile web search
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
Query Disambiguation Based on Novelty and Similarity User's Feedback
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Discovering and analyzing multi-granular web search results
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
| Hi-index | 0.00 |
We propose a novel conception language for exploring the results retrieved by several internet search services (like search engines) that cluster retrieved documents. The goal is to offer users a tool to discover relevant hidden relationships between clustered documents. The proposal is motivated by the observation that visualization paradigms, based on either the ranked list or clustered results, do not allow users to fully exploit the combined use of several search services to answer a request. When the same query is submitted to distinct search services, they may produce partially overlapped clustered results, where clusters identified by distinct labels collect some common documents. Moreover, clusters with similar labels, but containing distinct documents, may be produced as well. In such a situation, it may be useful to compare, combine and rank the cluster contents, to filter out relevant documents. In the proposed language, we define several operators (inspired by relational algebra) that work on groups of clusters. New clusters (and groups) can be generated by combining (i.e., overlapping, refining and intersecting) clusters (and groups), in a set oriented fashion. Furthermore, several ranking functions are also proposed, to model distinct semantics of the combination.