MobiMine: monitoring the stock market from a PDA
ACM SIGKDD Explorations Newsletter
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
ACM SIGMOD Record
Wireless Sensor Networks: From Data to Context to Energy Saving
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
A data stream-based evaluation framework for traffic information systems
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
Situation-Aware adaptive visualization for sensory data stream mining
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
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Ubiquitous Data Mining is the process of analysing data emanating from distributed and heterogeneous sources in the form of a continuous stream with mobile and/or embedded devices. Unsupervised learning is clearly beneficial for initial understanding of data streams, and consequently various clustering algorithms have been developed and applied in UDM systems for the purpose of mining data streams. However, unsupervised data mining techniques require human intervention for further understanding and analysis of the clustering results. This becomes an issue as UDM applications aim to support mobile and highly dynamic users/applications and there is a need for real-time decision making and interpretation of results. In this paper we present an approach to automate the annotation of results obtained from ubiquitous data stream clustering to facilitate interpreting and use of the results to enable real-time, mobile decision making.