Name
Abstract | ||
|---|---|---|
Evolutionary clustering is a hot research topic that clusters the time-stamped data and it is essential to some important applications such as data streams clustering and social network analysis. An evolutionary clustering should accurately reflect the current data at any time step while simultaneously not deviate too drastically from the recent past. In this paper, the differential evolution (DE) is applied to deal with the evolutionary clustering problem. Comparing with the typical k-means, evolutionary clustering based on DE (deEC) could perform a global search in the solution space. Experimental results over synthetic and real-world data sets demonstrate that the deEC provides robust and adaptive solutions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CEC.2014.6900488 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
pattern clustering,evolutionary computation,global search,evolutionary clustering based on de,time-stamped data clustering,evolutionary clustering problem,differential evolution,deec,vectors,statistics,history,clustering algorithms,sociology | Canopy clustering algorithm,CURE data clustering algorithm,Mathematical optimization,Clustering high-dimensional data,Correlation clustering,Computer science,Consensus clustering,Artificial intelligence,Brown clustering,Evolutionary programming,Cluster analysis,Machine learning | Conference |
Citations | PageRank | References |
4 | 0.43 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Chen | 1 | 97 | 15.41 |
| Wenjian Luo | 2 | 356 | 40.95 |
| Tao Zhu | 3 | 82 | 14.36 |