Name
Abstract | ||
|---|---|---|
Characteristics of data stream make it difficult for the clustering algorithms to satisfy the requirements on efficiency and effectiveness. This paper proposes a data stream clustering algorithm on dual-tier structure which employs the agent method. In the on-line process, a set of agents working simultaneously collect similar data points into sub-clusters by applying a heuristic strategy. And in the off-line process, summary information from the on-line component will be further analyzed to obtain the final clusters. The algorithm also supports the time-window queries on streams. The empirical evidence shows that this method can obtain high-quality clusters with low time complexity. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/GRC.2006.1635855 | GrC |
Keywords | Field | DocType |
agent,clustering,data mining,data stream,empirical evidence,computer science,telecommunications,satisfiability,streaming algorithm,time complexity,indexing terms,business,cluster analysis,algorithm design and analysis,clustering algorithms,information analysis | Data mining,Data stream mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Data stream clustering,Correlation clustering,Determining the number of clusters in a data set,Algorithm,Constrained clustering,Machine learning | Conference |
ISBN | Citations | PageRank |
1-4244-0134-8 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongbin Zhou | 1 | 2 | 0.79 |
| Lifeng Jia | 2 | 100 | 7.35 |
| Zhe Wang | 3 | 293 | 15.86 |
| Xiujuan Xu | 4 | 44 | 10.82 |
| Chunguang Zhou | 5 | 543 | 52.37 |