Name
Title | ||
|---|---|---|
A clustering algorithm for multiple data streams based on spectral component similarity |
Abstract | ||
|---|---|---|
We propose a new algorithm to cluster multiple and parallel data streams using spectral component similarity analysis, a new similarity metric. This new algorithm can effectively cluster data streams that show similar behaviour to each other but with unknown time delays. The algorithm performs auto-regressive modelling to measure the lag correlation between the data streams and uses it as the distance metric for clustering. The algorithm uses a sliding window model to continuously report the most recent clustering results and to dynamically adjust the number of clusters. Our experimental results on real and synthetic datasets show that our algorithm has better clustering quality, efficiency, and stability than other existing methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.ins.2011.09.004 | Inf. Sci. |
Keywords | Field | DocType |
recent clustering result,spectral component similarity analysis,existing method,parallel data stream,new similarity metric,multiple data stream,cluster multiple,new algorithm,clustering algorithm,data stream,clustering quality,cluster data stream,clustering,data streams | k-medians clustering,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Machine learning,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
183 | 1 | 0020-0255 |
Citations | PageRank | References |
26 | 0.86 | 41 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Chen | 1 | 86 | 6.43 |
| Lingjun Zou | 2 | 31 | 2.38 |
| Li Tu | 3 | 303 | 12.21 |