OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Concept decompositions for large sparse text data using clustering
Machine Learning
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Intelligent data analysis
Unsupervised Clustering In Streaming Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
SOStream: self organizing density-based clustering over data stream
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Interactive self-adaptive clutter-aware visualisation for mobile data mining
Journal of Computer and System Sciences
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The increasing availability of streaming data is a consequence of the continuing advancement of data acquisition technology. Such data provides new challenges to the various data analysis communities. Clustering has long been a fundamental procedure for acquiring knowledge from data, and new tools are emerging that allow the clustering of data streams. However the dynamic, temporal components of streaming data provide extra challenges to the development of stream clustering and associated visualisation techniques. In this work we combine a streaming clustering framework with an extension of a static cluster visualisation method, in order to construct a surface that graphically represents the clustering structure of the data stream. The proposed method, OpticsStream, provides intuitive representations of the clustering structure as well as the manner in which this structure changes through time.