Information Technology Reference
In-Depth Information
1.
Classification: The goal of classification is to predict the value of a user-
specified goal attribute based on the values of other attributes, called the
predicting attributes. This is the most studied data mining approach [6] [8].
2.
Clustering: In clustering applications, data mining algorithms must ''dis-
cover'' classes by partitioning the whole data set into several clusters, which
is a form of unsupervised learning[2].
3.
Associations: it is unique feature with the capability to find association rules
for items in a transaction file, and the capability to find all rules including
compound and hierarchical rule [2].
4.
Genetic Algorithm: Optimization techniques that use process such as genetic
combination, mutation, and natural selection in a design based on the con-
cepts of evolution [3].
5.
Decision Tree: Decision tree technique is one of the data mining methods
developed for classification and prediction. It is still of great help to reveal
explicit relationships between attributes among huge data. Many researchers
have been done with decision tree algorithm because of the great rule extrac-
tion and prediction ability [15].
3 Methods
3.1 Research Process
After clarifying the research background and objectives, we must now define the
process and architecture of the research. To achieve the research goal, left em-
ployees' database of a semiconductor company at the Hsinchu Science Park of
Taiwan will be used. The research architecture is shown in Figure 1, and the de-
scriptions of each stage are presented as below:
1.
Step 1: Exploring Data Analysis. The first stage to discover the record files
of layoff from the left employees' databases.
2.
Step 2: Constructing Organizational Network. Using social networks
analysis for constructing a organizational network from the left employees
databases. We use some network indicators, including density, degree, rea-
chability, centrality, position and role to analyze the relationship between
the manager and the laid-off employees.
3.
Step 3: Data Mining Analysis for the Layoff's file. Applying data mining
techniques for analysis the employees' attributes. We used the cluster analy-
sis to discover classes by partitioning the whole data set into several clusters
and used the association rules to discover the important associations among
items.
4.
Step 4: Constructing Layoff Prediction Model. Finally, we used the deci-
sion tree technique for classification and construction the layoff prediction
model from the laid-off employees' organizational networks.
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