Name
Abstract | ||
|---|---|---|
In data processing under dynamic environment such as stream, the time is one of the most significant facts not only because the size of data is dramatically increased but also because the context of data could be varied over time. To learn effectively from dynamic data evolving over time, it is required to detect the drift of the concept of data. We present a method to detect it by utilizing the correlation information of value distribution and apply our method to a learning task on a multi-stream data model. The result of experiments on a synthetic data set shows that our approach could provide a reasonable threshold to detect the change between windowed batches of stream data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/HPCC.2012.40 | HPCC-ICESS |
Keywords | Field | DocType |
stream data,reasonable threshold,concept drift,multi-stream data model,dynamic data,windowed batch,significant fact,correlation information,value distribution,dynamic environment,synthetic data,hardware,data models,vectors,data processing,correlation,data mining,machine learning,learning artificial intelligence | Data mining,Data modeling,Data processing,Data stream mining,Computer science,Stream data,Concept drift,Dynamic data,Synthetic data,Data model | Conference |
ISSN | Citations | PageRank |
2576-3504 | 6 | 0.44 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeonghoon Lee | 1 | 14 | 2.24 |
| Frederic Magoules | 2 | 91 | 6.46 |