• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1620-1624
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• 2010

The NLCCA has been applied to analyze the East Asia sea surface temperature (SST) and Korea monthly precipitation, where the eight leading PCs of the SST and the eight PCs of the precipitation during 1973-2007 were inputs to an NLCCA model. The first NLCCA mode is plotted in the PC spaces of the Korea precipitation and the world SST present a curve linking the nonlinear relationship between the first three leading PCs of Korea precipitation and world SST forthright. The correlation coefficient between canonical variate time series u and v is 0.8538 for the first NLCCA mode. And there are some areas' climate variability have higher relationship with Korea precipitation, especially focus on the north of East Sea' climate variability have represented the higher canonical correlation with Korea precipitation, with the correlation coefficient is 0.871 and 0.838. Likewise in Korea, most stations display similarly uniform distributing characteristic and less difference, in particular the inshore stations have display identical distributing characteristic. In correlation variables' scores, the fluctuation and variation trend are also seasonal oscillation with high frequency.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.371-384
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• 2012

This study proposes a multi-level data analysis approach to identify both superficial and latent relationships among variables in the data set obtained from a vocational rehabilitation (VR) services program of people with significant disabilities. At the first layer, data mining and statistical predictive models are used to extract the superficial relationships between dependent and independent variables. To supplement the findings and relationships from the analysis at the first layer, association rule mining algorithms at the second layer are employed to extract additional sets of interesting associative relationships among variables. Finally, nonlinear nonparametric canonical correlation analysis (NLCCA) along with clustering algorithm is employed to identify latent nonlinear relationships. Experimental outputs validate the usefulness of the proposed approach. In particular, the identified latent relationship indicates that disability types (i.e., physical and mental) and severity (i.e., severe, most severe, not severe) have a significant impact on the levels of self-esteem and self-confidence of people with disabilities. The identified superficial and latent relationships can be used to train education program designers and policy developers to maximize the outcomes of VR training programs.