Name
Abstract | ||
|---|---|---|
Advances in data acquisition have allowed large data collections of millions of time varying records in the form of data streams. The challenge is to effectively process the stream data with limited resources while maintaining sufficient historical information to define the changes and patterns over time. This paper describes an evidence-based approach that uses representative points to incrementally process stream data by using a graph based method to cluster points based on connectivity and density. Critical cluster features are archived in repositories to allow the algorithm to cope with recurrent information and to provide a rich history of relevant cluster changes if analysis of past data is required. We demonstrate our work with both synthetic and real world data sets. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68125-0_62 | PAKDD |
Keywords | Field | DocType |
real world data set,large data collection,stream data,data acquisition,critical cluster feature,process stream data,past data,relevant cluster change,data stream,localised density exemplar,cluster point | Data mining,Data set,Data stream mining,Data stream clustering,Computer science,Data stream,Data acquisition,Cluster analysis,Binary search tree,Dense graph | Conference |
Volume | ISSN | ISBN |
5012 | 0302-9743 | 3-540-68124-8 |
Citations | PageRank | References |
6 | 0.79 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Lühr | 1 | 123 | 8.04 |
| Mihai Lazarescu | 2 | 486 | 53.45 |