Data Quality Enhancement Technology to Improve Decision Support - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 72




xx x
xdx




()
()

Centroid= (4)

Where  is the aggregated output member function. The details of Fuzzy system have

been explained in [20] [23].

()

3. Experiments and results

The dataset for preprocessing is taken from Politecnico in Torino (Italy) that contains two
attributes, temperature and humidity. These measures have been taken from a sensor and
recorded by a computer at regular intervals of about 15 minutes (the average interval was
estimated at 896 seconds) [24]. The dataset has been preprocessed by WEKA software to
check missing value. And the statistic equation was utilized to remove noisy data, after that
FCM clustering was used to detect outliers. By detecting outliers, desired data was extracted
from dataset as an input of fuzzy system for controlling and decision making.
The Italy dataset possesses 4600 instances. Based on the equation coded in the Matlab, the
noise was found and removed from the dataset. The graphic view of the Italy dataset before
removing noise is depicted in Figure below.

Fig. 3. Graphical View of the Original Italy Dataset
As shown in Figure 3, the attributes (temperature 1 and 2) were derived from the device
sensor. The extracted data may include noisy data due to device and measurement errors.
This would decrease the quality of the data. Therefore, the program in this study was
applied based on the definition of the statistic equation on the data to remove the noisy data.
The characteristics and results are shown in Table 1.