Information Technology Reference
In-Depth Information
3.3 Data Mining Analysis for Layoff's File
In this session, in order to construct the layoff prediction model, we used the data
mining techniques for extracting rules from selected data. This research used 124
training data from the laid-off employees network of a semiconductor company in
the Hsinchu Taiwan Science Park. The testing data is 100 employees' data of the
active employees' database from the same resource in the year 2009. Each record
in employees' database consists of 15attributes. The original attributes of each
column are as follows.
Table 2
The Attributes List
Attribute
Attribute
Attribute
6.
Compensation _LV
1.ID
11. Hire_DT
2. Name
7. Live register
12.Termination_DT
3. Dept_ID
8. Education_LV
13.Supervisor_ID
4. Sex
9. Marriage
14. Position
5.Age
10. Grade
15. Shift_DESCR
The data mining techniques involved in this research are demonstrated, includ-
ing feature selection techniques for diminishing the data dimension. The classifi-
cation analysis and association rule for extracting rules from selected data. The
descriptions of each analysis as followed:
1.
Clustering:
Clustering is the task of segmenting a heterogeneous population
into a number of more homogeneous subgroups or clusters. According to the
attributes, we selected age, sex, marriage, grade, education level, shift, posi-
tion and compensation level to cluster the left employee 6 segments by K-
Means. To keep all clusters almost the same number of employees, we
firstly divided each variable into four parts by quantification. We then trans-
formed these numeric data to be categorical one for clustering.
2.
Association Rule:
Association analysis is the discovery of association rules
showing attribute value conditions that occur frequently together in a given
set of data. The layoff of association rules are generated from WEKA tool
that was developed by University of Waikato in New Zealand. We used
WEKA to do association and found the 8 useful rules for the laid-off em-
ployees' attribute.
3.
Decision Tree:
A decision tree divide the records in the training set into dis-
joint subsets, each of which is described by a simple rule on one or more
fields [3]. In this research, the training data set contains 124 records and the
testing dataset has 100 records. This research combines the training dataset
and testing dataset into a table.
