OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Computing standard deviations: accuracy
Communications of the ACM
Eye tracking in web search tasks: design implications
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
A Parameterless Method for Efficiently Discovering Clusters of Arbitrary Shape in Large Datasets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The determinants of web page viewing behavior: an eye-tracking study
Proceedings of the 2004 symposium on Eye tracking research & applications
Multidocument summarization: An added value to clustering in interactive retrieval
ACM Transactions on Information Systems (TOIS)
Proceedings of the 13th international conference on World Wide Web
Subspace clustering for high dimensional categorical data
ACM SIGKDD Explorations Newsletter
EyeWindows: evaluation of eye-controlled zooming windows for focus selection
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Conversing with the user based on eye-gaze patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Highly scalable trip grouping for large-scale collective transportation systems
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
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The study of the use of computers through human computer interfaces (HCI) is essential to improve the productivity in any computer application environment. HCI analysts use a number of techniques to build models that are faithful to actual computer use. A key technique is through eye tracking, in which the region of the screen being examined is recorded in order to determine key areas of use. Clustering techniques allow these regions to be grouped to help facilitate usability analysis. Historically, approaches such as the Expectation Maximization (EM) and K-Means algorithm have performed well. Unfortunately, these approaches require the number of clusters k to be known beforehand – in many real world situations, this hampers the effectiveness of the analysis of the data. We propose a novel algorithm that is well suited for cluster discovery for HCI data; we do not require the number of clusters to be specified a priori and our approach scales very well for both large datasets and high dimensionality. Experiments have demonstrated that our approach works well for real data from HCI applications.