Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Clustering on Demand for Multiple Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Unified Framework for Monitoring Data Streams in Real Time
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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In this paper, we present a framework for event-driven Clustering Over Multiple Evolving sTreams, which, abbreviated as COMET, monitors the distribution of clusters on multiple data streams and online reports the results. This information is valuable to support corresponding online decisions. Note that as time advances, the data streams are evolving and the clusters they belong to will change. Instead of directly clustering the multiple data streams periodically, COMET applies an efficient cluster adjustment procedure only when it is required. The signal of requiring to do cluster adjustments is defined as an ”event.” We design a mechanism of event detection which employs piecewise linear approximation as the key technique. The piecewise linear approximation is advantageous in that it can not only be performed in real time as the data comes in, but also be able to capture the trend of data. When an event occurs, through split and merge operations we can report the latest clustering results effectively with high clustering quality.