Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Issues in data stream management
ACM SIGMOD Record
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Rough set based incremental clustering of interval data
Pattern Recognition Letters
| Hi-index | 0.10 |
In stream monitoring applications, it is important to identify rapidly abnormal events over bursty data arrivals. By clustering similar conditions used in event detection, it is possible to reduce the number of comparisons and improve the event detection performance. On the other hand, event detection based on these clustered conditions can produce inaccurate results. Therefore, to use this method for critical applications, such as patient monitoring, the number of event detection errors needs to be kept to within a tolerable level. This paper presents an interval clustering algorithm that provides an error control mechanism. The proposed algorithm enables a user to specify a permissible error bound, and then uses the bound as a threshold condition for clustering. The simulation conducted based on real data showed that the algorithm improves the performance of event detection by clustering conditions while observing a user-specified error bound.