Name
Abstract | ||
|---|---|---|
A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator
selection. In the proposed method, the category of each indicator is coded by real numbers, and some illegal chromosomes are
repaired by the identification and restoration of empty class. Two mutation operators, namely the discrete random mutation
operator and the optimal direction mutation operator, are designed to balance the local convergence speed and the global convergence
performance, which are then combined with migration strategy and insertion strategy. For the purpose of verification and illustration,
the proposed method is compared with the K-means clustering algorithm and the standard genetic algorithms via a numerical
simulation experiment. The experimental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis
algorithm. Meanwhile, the proposed clustering analysis algorithm is also applied to select the business cycle indicators to
examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly
select some leading indicators, coincident indicators, and lagging indicators to reflect the business cycle, which is extremely
operational for some macro economy administrative managers and business decision-makers. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11704-009-0023-5 | Frontiers of Computer Science in China |
Keywords | Field | DocType |
Genetic algorithm,pseudo parallel genetic algorithm,clustering analysis,business cycle | Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Population-based incremental learning,Genetic algorithm,Canopy clustering algorithm,Mathematical optimization,Correlation clustering,Local convergence,Macro,Machine learning | Journal |
Volume | Issue | ISSN |
3 | 2 | 16737466 |
Citations | PageRank | References |
1 | 0.40 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dabin Zhang | 1 | 3 | 1.45 |
| Lean Yu | 2 | 1777 | 179.93 |
| Shouyang Wang | 3 | 2396 | 219.80 |
| Yingwen Song | 4 | 3 | 2.19 |