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Toward Successful Management of Vocational Rehabilitation Services for People with Disabilities: A Data Mining Approach
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 Title & Authors
Toward Successful Management of Vocational Rehabilitation Services for People with Disabilities: A Data Mining Approach
Kim, Yong Seog;
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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.
Vocational Rehabilitation Services;Data Mining;Classification;Association Rule;Clustering;NLCCA;
 Cited by
Agrawal, R. and Srikant, R. (1994), Fast algorithms for mining association rules, Proceedings of the 20th Very Large Data Bases Conference, Santiago, Chile, 487-499.

Anandarajan, M. (2002), Profiling Web usage in the workplace: a behavior-based artificial intelligence approach, Journal of Management Information Systems, 19(1), 243-266. crossref(new window)

Aumann, Y. and Lindell, Y. (2003), A statistical theory for quantitative association rules, Journal of Intelligent Information Systems, 20(3), 255-283. crossref(new window)

Breiman, L. (1996), Bagging predictors, Machine Learning, 24(2), 123-140.

Bandura, A. (1986), Social Foundations of Thought and Action: A Social Cognitive Theory, Prentice-Hall, Englewood Cliffs, NJ.

Bandura, A., Caprara, G. V., Barbaranelli, C., Gerbino, M., and Pastorelli, C. (2003), Role of affective selfregulatory efficacy in diverse spheres of psychosocial functioning, Child Development, 74(3), 769- 782. crossref(new window)

Benbasat, I. and Zmud, R. W. (2003), The identity crisis within the is discipline: defining and communicating the discipline's core properties, MIS Quarterly, 27(2), 183-194. crossref(new window)

Brijs, T., Swinnen, G., Wanhoof, K., and Wets, G. (1999), Using association rules for product assortment decisions: a case study, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, 254- 260.

Clark, D. B., Moss, H. B., Kirisci, L., Mezzich, A. C., Miles, R., and Ott, P. (1997), Psychopathology in preadolescent sons of fathers with substance use disorders, Journal of the American Academy of Child & Adolescent Psychiatry, 36(4), 495-502. crossref(new window)

Cox, R. H. (1998), Sport Psychology: Concepts and Applications, McGraw-Hill, Boston, MA.

Edwards, T. and Hardy, L. (1996), The interactive effects of intensity and direction of cognitive somatic anxiety and self-confidence upon performance, Journal of Sport and Exercise Psychology, 18(3), 296-312. crossref(new window)

Flach, P. A. and Lachiche, N. (2001), Confirmationguided discovery of first-order rules with Tertius, Machine Learning, 42(1-2), 61-95. crossref(new window)

Freund, Y. and Schapire, R. E. (1996), Experiments with a new boosting algorithm, Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, 148-156.

Jain, A. K., Murty, M. N., and Flynn, P. J. (1999), Data clustering: a review, ACM Computing Surveys, 31(3), 264-323. crossref(new window)

Kim, Y. S. (2009), Streaming association rule (SAR) mining with a weighted order-dependent representation of Web navigation patterns, Expert Systems with Applications, 36(4), 7933-7946. crossref(new window)

Mazid, M. M., Shawkat Ali, A. B. M., and Tickle, K. S. (2008), Finding a unique association rule mining algorithm based on data characteristics, Proceedings of the International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, 902-908.

Miyahara, M. and Piek, J. (2006), Self-esteem of children and adolescents with physical disabilities: quantitative evidence from meta-analysis, Journal of Developmental and Physical Disabilities, 18(3), 219-234. crossref(new window)

Miyahara, M. and Register, C. (2000), Perceptions of three terms to describe physical awkwardness in children, Research in Developmental Disabilities, 21(5), 367-376. crossref(new window)

Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2002), Discovery and evaluation of aggregate usage profiles for Web personalization, Data Mining and Knowledge Discovery, 6(1), 61-82. crossref(new window)

Padmanabhan, B. and Tuzhilin, A. (1999), Unexpectedness as a measure of interestingness in knowledge discovery, Decision Support Systems, 27(3), 303- 318. crossref(new window)

Scheffer, T. (2005), Finding association rules that trade support optimally against confidence, Intelligent Data Analysis, 9(4), 381-395.

Silberschatz, A. and Tuzhilin, A. (1996), What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering, 8(6), 970-974. crossref(new window)

Silverstein, C., Brin, S., and Motwani, R. (1998), Beyond market baskets: generalizing association rules to dependence rules, Data Mining and Knowledge Discovery, 2(1), 39-68. crossref(new window)

Spiliopoulou, M., Pohle, C., and Faulstich, L. C. (2000), Improving the effectiveness of a web site with web usage mining, Web Usage Analysis and User Profiling, Lecture Notes in Computer Science, 1836, 142-162.

ter Braak, C. J. F. (1990), Interpreting canonical correlation analysis through biplots of structure correlations and weights, Psychometrika, 55(3), 519-531. crossref(new window)

Thomas, A. P., Bax, M., and Smyth, D. P. L. (1989), The Health and Social Needs of Young Adults with Physical Disabilities, MacKeith, London, UK.

Vogt, C. M., Hocevar, D., and Hagedorn, L. S. (2007),A social cognitive construct validation: determining women's and men's success in engineering programs, Journal of Higher Education, 78(3), 337- 364. crossref(new window)

Zimmerman, B. J., Bandura, A., and Martinez-Pons, M. (1992), Self-motivation for academic attainment: the role of self-efficacy beliefs and personal goal setting, American Educational Research Journal, 29(3), 663-676. crossref(new window)