Name
Abstract | ||
|---|---|---|
In this paper, we propose an algorithm for cluster analysis inspired by the lymphocyte-cytokine network in the immune system. Our algorithm attempts to optimally represent a large data set by its principle subset whilst maximising the data kernel density distribution. Experiments show that the output data set created by our algorithm effectively represents the original input data set, according to the Kullback-Leibler divergence metric. We compare the performance of our approach with the well-known aiNet algorithm and find our approach provides a significant improvement on the representation of the final data set. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22371-6_18 | ICARIS |
Keywords | Field | DocType |
data analysis,well-known ainet algorithm,large data,output data,immune system,data kernel density distribution,final data,lymphocyte-cytokine network,kullback-leibler divergence metric,algorithm attempt,original input data,cluster analysis | Cytokine Network,Data mining,Mathematical optimization,Data compression ratio,Computer science,Algorithm,FSA-Red Algorithm,Artificial intelligence,Clonal selection algorithm,Machine learning,Kernel density estimation | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Liu | 1 | 11 | 2.31 |
| Jon Timmis | 2 | 1237 | 120.32 |
| Tim Clarke | 3 | 102 | 20.02 |