|
|
A novel PPGA-based clustering analysis method for business cycle indicator selection |
Dabin ZHANG1,2, Lean YU2( ), Shouyang WANG2, Yingwen SONG3 |
1. Department of Information Management, Huazhong Normal University, Wuhan 430079, China; 2. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; 3. Information Technology Research Institute, National Advanced Industrial Science and Technology, Ibaraki 305-8568, Japan |
|
|
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.
|
Keywords
Genetic algorithm
pseudo parallel genetic algorithm
clustering analysis
business cycle
|
Corresponding Author(s):
YU Lean,Email:yulean@amss.ac.cn
|
Issue Date: 05 June 2009
|
|
1 |
Layton A P, Moore G H. Leading indicators for the service sector. Journal of Business and Economic Statistics , 1989, 7(3): 379-386 doi: 10.2307/1391534
|
2 |
Stock J H, Watson M W. New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual 1989 , 1989: 351-394
|
3 |
Banerji A, Hiris L. A framework for measuring international business cycles. International Journal of Forecasting , 2001, 17: 333-348 doi: 10.1016/S0169-2070(01)00089-9
|
4 |
Farley A M, Jones S. Using a genetic algorithm to determine an index of leading economic indicators. Computational Economics , 1994, 7(3): 163-173
|
5 |
Dai W H, Jiao C Z, He T T. Research of K-means clustering method based on parallel genetic algorithm. In: Proceedings of the 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing , 2007, (2): 158-161
|
6 |
Selim S Z, Alsultmi K. A simulated annealing algorithm for the clustering problem. Pattern Recognition , 1991, 24(10): 1003-1008 doi: 10.1016/0031-3203(91)90097-O
|
7 |
Güng?r Z, ünler A. K-harmonic means data clustering with simulated annealing heuristic. Applied Mathematics and Computation , 2007, 184(2): 199-209 doi: 10.1016/j.amc.2006.05.166
|
8 |
Duczmal L, Assun??o R. A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters. Computational Statistics & Data Analysis , 2004, 45(2): 269-286 doi: 10.1016/S0167-9473(02)00302-X
|
9 |
Paterlini S, Krink T. Differential evolution and particle swarm optimization in partitional clustering. Computational Statistics & Data Analysis , 2006, 50(5): 1220-1247 doi: 10.1016/j.csda.2004.12.004
|
10 |
Das D, Abraham A, Konar A. Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognition Letters , 2008, 29(5): 688-699 doi: 10.1016/j.patrec.2007.12.002
|
11 |
Liao Z Z, Luo K, Zhou F H, Fu P. Cluster algorithm based on parallel particle swarm optimizer using adaptive inertia weight. Computer Engineering and Applications , 2007, 3: 166-168
|
12 |
Xu X H, Chen L. An adaptive ant clustering algorithm. Journal of Software , 2006, 17(9): 1884-1889 doi: 10.1360/jos171884
|
13 |
Li R, Qiu R Y. Study of Ants-Clustering algorithm based on outlier. Computer Science , 2005, 32(6): 111-114
|
14 |
Roberts C, Johnston R L, Wilson N T. A genetic algorithm for the structural optimization of Morse clusters. Theoretical Chemistry Accounts , 2000, 104(2): 123-130 doi: 10.1007/s002140000117
|
15 |
Qing L, . Crowding clustering genetic algorithm for multimodal function optimization. Applied Soft Computing , 2008, 8(1): 88-95 doi: 10.1016/j.asoc.2006.10.014
|
16 |
Firat A, Chatterjee S, Yilmaz M. Genetic clustering of social networks using random walks. Computational Statistics & Data Analysis , 2007, 51(12): 6285-6294 doi: 10.1016/j.csda.2007.01.010
|
17 |
Tseng L Y, Yang S B. A genetic clustering algorithm for data with non-spherical-shape clusters. Pattern Recognition , 2000, 33(7): 1251-1259 doi: 10.1016/S0031-3203(99)00105-3
|
18 |
Bosco G L. PGAC, a parallel genetic algorithm for data clustering. In: Proceedings of the Seventh International Workshop on Computer Architecture for Machine Perception , 2005: 283-287
|
19 |
Garai G, Chaudhuri B B. A novel genetic algorithm for automatic clustering. Pattern Recognition Letters , 2004, 25(2): 173-187 doi: 10.1016/j.patrec.2003.09.012
|
20 |
Kivij?rvi J, Fr?nti P, Nevalainen O. Self-adaptive genetic algorithm for clustering. Journal of Heuristics , 2003, 9(2): 113-129 doi: 10.1023/A:1022521428870
|
21 |
Hang WZ, Yin X G, Zhang Z, Yang J C. Pseudo-parallel genetic algorithm for reactive power optimization. In: Proceedings of IEEE Power Engineering Society General Meeting , 2003, (2): 13-17
|
22 |
Yang Y, Vincent J, Littlefair G. A coarse-grained parallel genetic algorithm employing cluster analysis for multi-modal numerical optimization. Lecture Notes in Computer Science , 2004, 2936: 229-240
|
23 |
Moore M. An accurate parallel genetic algorithm to schedule tasks on a cluster. Parallel Computing , 2004, 30(5-6): 567-583 doi: 10.1016/j.parco.2003.12.005
|
24 |
Ding J L, Tang W S, Wang L Q. Parallel combination of genetic algorithm and ant algorithm based on dynamic K-means cluster. In: Proceedings of International Conference on Computational Intelligence , 2006, 4114: 825-830
|
25 |
Maulik U, Bandyopadhyay S. Genetic algorithm-based clustering technique. Pattern Recognition , 2000, 33(9): 1455-1465 doi: 10.1016/S0031-3203(99)00137-5
|
26 |
Krishma K, Murty M N. Genetic k-means algorithm. IEEE Transaction on Systems Man and Cybernetics-Part B , 1999, 29 (3): 433-439 doi: 10.1109/3477.764879
|
27 |
Issler J V, Vahid F. The missing link: using the NBER recession indicator to construct coincident and leading indices of economic activity. Journal of Econometrics , 2006, 132(1): 281-303 doi: 10.1016/j.jeconom.2005.01.031
|
28 |
Wang X Z, Smith K A, Hyndman R J. Characteristic-based clustering for time series data. Data mining and knowledge Discovery , 2006, 13(3): 335-364 doi: 10.1007/s10618-005-0039-x
|
29 |
Dong WQ, . Analysis and Forecasting Methods of Economic Cycles. Jilin University Press , 1998, 8: 182-192
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|