Journal of Systems Engineering and Electronics 2010, 21(1) 142-147 DOI:   10.3969/j.issn.1004-4132.2010.01.023  ISSN: 1004-4132 CN: 11-3018/N

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Keywords
modeling
missing value
K-means with soft constraints clustering
missing value insensitive kernel
Authors
PubMed

Modelling method with missing values based on clustering and support vector regression

Ling Wang∗, Dongmei Fu, Qing Li, and Zhichun Mu

Information Engineering School, University of Science and Technology Beijing, Beijing 100083, P. R. China

Abstract��

Most real application processes belong to a complex nonlinear system with incomplete information. It is difficult to
estimate a model by assuming that the data set is governed by a global model. Moreover, in real processes, the available data set is usually obtained with missing values. To overcome the shortcomings of global modeling and missing data values, a new modeling method is proposed. Firstly, an incomplete data set with missing values is partitioned into several clusters by a K-means with soft constraints (KSC) algorithm, which incorporates soft constraints to enable clustering with missing values. Then a local model based on each group is developed by using SVR algorithm, which adopts a missing value insensitive (MVI) kernel to investigate the missing value estimation problem. For each local model, its valid area is gotten as well. Simulation results prove the effectiveness of the current local model and the estimation algorithm.

Keywords�� modeling   missing value   K-means with soft constraints clustering   missing value insensitive kernel  
Received  Revised  Online:  
DOI: 10.3969/j.issn.1004-4132.2010.01.023
Fund:

This work was supported by Key Discipline Construction Program of Beijing Municipal Commission of Education (XK10008043).

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