Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Full text information processing using the smart system
Data Engineering
Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning collection fusion strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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
Experiences with selecting search engines using metasearch
ACM Transactions on Information Systems (TOIS)
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Methods for information server selection
ACM Transactions on Information Systems (TOIS)
Cluster-based language models for distributed retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A decision-theoretic approach to database selection in networked IR
ACM Transactions on Information Systems (TOIS)
GlOSS: text-source discovery over the Internet
ACM Transactions on Database Systems (TODS)
Database merging strategy based on logistic regression
Information Processing and Management: an International Journal
Evaluating document clustering for interactive information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Merging techniques for performing data fusion on the web
Proceedings of the tenth international conference on Information and knowledge management
Using sampled data and regression to merge search engine results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Fusion Via a Linear Combination of Scores
Information Retrieval
The effectiveness of query-specific hierarchic clustering in information retrieval
Information Processing and Management: an International Journal
Determining Text Databases to Search in the Internet
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Server Ranking for Distributed Text Retrieval Systems on the Internet
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
The Effectiveness and Efficiency of Agglomerative Hierarchic Clustering in Document Retrieval
The Effectiveness and Efficiency of Agglomerative Hierarchic Clustering in Document Retrieval
Distributed information retrieval: a multi-objective resource selection approach
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Intelligent information systems
Retrieval result presentation and evaluation
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Cluster-based fusion of retrieved lists
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
From "identical" to "similar": fusing retrieved lists based on inter-document similarities
Journal of Artificial Intelligence Research
The opposite of smoothing: a language model approach to ranking query-specific document clusters
Journal of Artificial Intelligence Research
Utilizing inter-document similarities in federated search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Hi-index | 0.00 |
How to merge and organise query results retrieved from different resources is one of the key issues in distributed information retrieval. Some previous research and experiments suggest that cluster-based document browsing is more effective than a single merged list. Cluster-based retrieval results presentation is based on the cluster hypothesis, which states that documents that cluster together have a similar relevance to a given query. However, while this hypothesis has been demonstrated to hold in classical information retrieval environments, it has never been fully tested in heterogeneous distributed information retrieval environments. Heterogeneous document representations, the presence of document duplicates, and disparate qualities of retrieval results, are major features of an heterogeneous distributed information retrieval environment that might disrupt the effectiveness of the cluster hypothesis. In this paper we report on an experimental investigation into the validity and effectiveness of the cluster hypothesis in highly heterogeneous distributed information retrieval environments. The results show that although clustering is affected by different retrieval results representations and quality, the cluster hypothesis still holds and that generating hierarchical clusters in highly heterogeneous distributed information retrieval environments is still a very effective way of presenting retrieval results to users.