Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
A clustering algorithm for asymmetrically related data with applications to text mining
Proceedings of the tenth international conference on Information and knowledge management
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 13th international conference on World Wide Web
A personalized search engine based on web-snippet hierarchical clustering
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
A clustering method for news articles retrieval system
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Full-Subtopic Retrieval with Keyphrase-Based Search Results Clustering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Faceted Search
Comprehensible and accurate cluster labels in text clustering
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Search result diversity for informational queries
Proceedings of the 20th international conference on World wide web
Beyond precision@10: clustering the long tail of web search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Cluster generation and cluster labelling for web snippets: a fast and accurate hierarchical solution
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
ChatNoir: a search engine for the ClueWeb09 corpus
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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We propose a competence partitioning strategy for Web search result presentation: the unmodified head of a ranked result list is combined with a clustering of documents from the result list tail. We identify two principles to which such a clustering must adhere to improve the user's search experience: (1) Avoid the unwanted effect of query aspect repetition, which is called shadowing here. (2) Avoid extreme clusterings, i.e., neither the number of cluster labels nor the number of documents per cluster should exceed the size of the result list head. We present measures to quantify the shadowing effect, and with Faceted Clustering we introduce an algorithm that optimizes the identified principles. The key idea of Faceted Clustering is a dynamic, user-controlled reorganization of a clustering, similar to a faceted navigation system. We report on evaluations using the AMBIENT corpus and demonstrate the potential of our approach by a comparison with two well-known clustering search engines.