Name
Title | ||
|---|---|---|
Interval clustering algorithm for fast event detection in stream monitoring applications |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.patrec.2013.09.017 | Pattern Recognition Letters |
Keywords | Field | DocType |
event detection error,stream monitoring application,permissible error,event detection,clustering condition,event detection performance,error control mechanism,abnormal event,fast event detection,proposed algorithm,user-specified error,interval clustering algorithm,error control,data streams | Data mining,CURE data clustering algorithm,Data stream mining,Data stream clustering,Pattern recognition,Computer science,Remote patient monitoring,Error detection and correction,Artificial intelligence,Cluster analysis,Tolerable Level | Journal |
Volume | ISSN | Citations |
36, | 0167-8655 | 2 |
PageRank | References | Authors |
0.38 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyeon Gyu Kim | 1 | 14 | 5.03 |
| Cheolgi Kim | 2 | 75 | 13.38 |