In this paper, we propose a partitional approach for estimating null value (1) Firstly, we utilize stepwise regression to select the important attributes from the database. (2) Secondly, we use a partitional approach to build the data category. The data partitioned by the first two important attributes. (3) Thirdly, we apply the clustering method to cluster output data. (4) Fourthly, Calculate the degree of influential between the attributes. There are two ways to calculate the degree of influential. One is correlation coefficient and the other is regression coefficients. (5) To verify our method, this paper utilizes a practical human resource database in Taiwan, and Mean of Absolute Error Rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves better than other methods for estimating null values in relational database systems.
[1]
Yair M. Babad,et al.
Even no data has a value
,
1984,
CACM.
[2]
N. Draper,et al.
Applied Regression Analysis
,
1967
.
[3]
N. Draper,et al.
Applied Regression Analysis: Draper/Applied Regression Analysis
,
1998
.
[4]
J. MacQueen.
Some methods for classification and analysis of multivariate observations
,
1967
.
[5]
Shyi-Ming Chen,et al.
A new method to estimate null values in relational database systems based on automatic clustering techniques
,
2005,
Inf. Sci..
[6]
N. Draper,et al.
Applied Regression Analysis
,
1966
.
[7]
Jiawei Han,et al.
Data Mining: Concepts and Techniques
,
2000
.