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A semi-supervised regression model for mixed numerical and categorical variables

Record type: Article

In this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-mode clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-mode parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least-square errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method.

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Citation

Ng, Michael K., Chan, Elaine Y., So, M.C. and Ching, Wai-Ki (2007) A semi-supervised regression model for mixed numerical and categorical variables Pattern Recognition, 40, (16), pp. 1745-1752. (doi:10.1016/j.patcog.2006.06.018).

More information

Published date: 1 June 2007
Keywords: clustering, regression, data mining, numerical variables, categorical variables

Identifiers

Local EPrints ID: 180719
URI: http://eprints.soton.ac.uk/id/eprint/180719
ISSN: 0031-3203
PURE UUID: 47e8a94e-764f-4643-a82b-ae48832aeb21

Catalogue record

Date deposited: 13 Apr 2011 14:53
Last modified: 18 Jul 2017 12:00

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Contributors

Author: Michael K. Ng
Author: Elaine Y. Chan
Author: M.C. So
Author: Wai-Ki Ching

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