Name
Abstract | ||
|---|---|---|
We present a new algorithm for discovering clusters in noisy data streams using dynamic and cluster-specific temporal decay factors. Our improvement helps identify and adapt to evolving trends by adapting the weighting of stream data based on both content attributes and temporal arrival patterns. Our experimental results show that the proposed algorithm can discover better quality clusters in noisy data streams with varying configurations and temporal dynamics. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BigDataCongress.2017.72 | 2017 IEEE International Congress on Big Data (BigData Congress) |
Keywords | Field | DocType |
Data Stream Clustering,dynamic Clustering | Forgetting,Data mining,Data modeling,Data stream mining,Weighting,Data stream clustering,Pattern recognition,Computer science,Robustness (computer science),Artificial intelligence,STREAMS,Cluster analysis | Conference |
ISSN | ISBN | Citations |
2379-7703 | 978-1-5386-1997-1 | 0 |
PageRank | References | Authors |
0.34 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gopi Chand Nutakki | 1 | 4 | 2.55 |
| Olfa Nasraoui | 2 | 1515 | 164.53 |