The spatial metaphor for user interfaces: experimental tests of reference by location versus name
ACM Transactions on Information Systems (TOIS)
A “pile” metaphor for supporting casual organization of information
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Scatter/gather browsing communicates the topic structure of a very large text collection
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data mountain: using spatial memory for document management
Proceedings of the 11th annual ACM symposium on User interface software and technology
Visualizing implicit queries for information management and retrieval
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Building bridges: customisation and mutual intelligibility in shared category management
GROUP '99 Proceedings of the international ACM SIGGROUP conference on Supporting group work
How do people organize their desks?: Implications for the design of office information systems
ACM Transactions on Information Systems (TOIS)
The character, value, and management of personal paper archives
ACM Transactions on Computer-Human Interaction (TOCHI)
The Journal of Machine Learning Research
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Spatial Tools for Managing Personal Information Collections
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
Keepin' it real: pushing the desktop metaphor with physics, piles and the pen
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 12th international conference on Intelligent user interfaces
Bubble clusters: an interface for manipulating spatial aggregation of graphical objects
Proceedings of the 20th annual ACM symposium on User interface software and technology
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
iVisClustering: An Interactive Visual Document Clustering via Topic Modeling
Computer Graphics Forum
Personalized document clustering with dual supervision
Proceedings of the 2012 ACM symposium on Document engineering
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Sorting and clustering large numbers of documents can be an overwhelming task: manual solutions tend to be slow, while machine learning systems often present results that don't align well with users' intents. We created and evaluated a system for helping users sort large numbers of documents into clusters. iCluster has the capability to recommend new items for existing clusters and appropriate clusters for items. The recommendations are based on a learning model that adapts over time - as the user adds more items to a cluster, the system's model improves and the recommendations become more relevant. Thirty-two subjects used iCluster to sort hundreds of data items both with and without recommendations; we found that recommendations allow users to sort items more rapidly. A pool of 161 raters then assessed the quality of the resulting clusters, finding that clusters generated with recommendations were of statistically indistinguishable quality. Both the manual and assisted methods were substantially better than a fully automatic method.