Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
MobiMine: monitoring the stock market from a PDA
ACM SIGKDD Explorations Newsletter
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Cost-efficient mining techniques for data streams
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Context-Aware ubiquitous data mining based agent model for intersection safety
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
| Hi-index | 0.00 |
There is currently a growing new focus in data mining – Ubiquitous Data Mining (UDM). UDM is the process of mining data streams in a ubiquitous environment, on resource constrained devices [KPP02]. UDM is widely applied in facilitating real-time decision making in mobile and highly dynamic environments/applications, such as road safety and mobile stock portfolio monitoring. A significant challenge in these contexts is the interpretation and analysis of results produced through unsupervised techniques (which are invaluable since little is known about the streamed data). We propose a novel fuzzy approach that leverages the significant benefits of UDM clustering and supplements the interpretation and use of these results through using expert/background knowledge.