Principles of data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-Organizing Maps
Advanced Interaction in Context
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Unsupervised Clustering of Symbol Strings and Context Recognition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
An ontology for context-aware pervasive computing environments
The Knowledge Engineering Review
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
An ontology for mobile device sensor-based context awareness
CONTEXT'03 Proceedings of the 4th international and interdisciplinary conference on Modeling and using context
SmartActions: Context-Aware Mobile Phone Shortcuts
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part I
User profiling with hierarchical context: an e-Retailer case study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Exploiting the icon arrangement on mobile devices as information source for context-awareness
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
An unsupervised learning paradigm for peer-to-peer labeling and naming of locations and contexts
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Situation-Aware on mobile phone using co-clustering: algorithms and extensions
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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The K-SCM is an unsupervised learning algorithm, designed to cluster symbol string data in an on-line manner. Unlike many other learning algorithms there are no time dependent gain factors. Context recognition based on the fusion of information sources is formulated as the clustering of symbol string data. Applied to real measured context data it is shown how the clusters can be associated with higher level contexts. This unsupervised learning approach is fundamentally different to the approach based, for example, on ontologies or supervised learning. Unsupervised learning requires no intervention from an outside expert. Using the example of menu adaptation in a mobile device, and a second learning stage, it is shown how user requirements in a given context can be associated with the learned contexts. This approach can be used to facilitate user interaction with the device.