Name
Title | ||
|---|---|---|
Optimization of water quality monitoring section based on comprehensive hierarchical clustering |
Abstract | ||
|---|---|---|
In order to optimize the section layout of water quality monitoring, this paper proposes a new method based on comprehensive hierarchical clustering (CHC). Firstly, the method calculated the affinity-disaffinity relationship among the monitoring variables through 5 distance algorithms. Afterwards, the data set could be clustered automatically through 4 connection algorithms. Then taking the correlation coefficient as evaluation criteria, optimal hierarchical clustering algorithm was selected. Finally, with the corresponding optimal clustering tree matrix, the monitoring sections can be set optimally. In addition, the paper used student's t test to verify the result of optimization. The experimental results show that this method can reflect the water quality of whole area more efficiently, thus has good prospect. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/FSKD.2015.7381927 | 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) |
Keywords | Field | DocType |
water quality monitoring,section optimization,comprehensive hierarchical clustering (CHC),evaluation criteria of correlation coefficient,student's t test | Data mining,CURE data clustering algorithm,Matrix (mathematics),Computer science,Artificial intelligence,Student's t-test,Cluster analysis,Water quality,Hierarchical clustering,Correlation coefficient,Pattern recognition,Correlation clustering,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sen Peng | 1 | 0 | 0.34 |
| Xiaofeng Lian | 2 | 2 | 1.76 |
| Xiaoyi Wang | 3 | 37 | 16.96 |
| Jiping Xu | 4 | 3 | 5.50 |