Data Quality Enhancement Technology to Improve Decision Support - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 74

The attributes, which were derived after removing noise and clustered based on the FCM
algorithm, are shown in Figure 4. The number of clusters is 2 (k=2, k is a number of cluster);
green represents one cluster and red is another cluster which possesses behaviour that is
different from each other.

Dataset Number
of
cluster

Number
of
Instances
with noise

Number
of noisy
instance

Number
of
instance
without
noise

Center of Cluster
(Mean)
Cluster 1 Cluster 2 Cluster 1
(x,y)

Number of
instances

Cluster 2
(x,y)

Italy 2 4600 90 4510 566 3944 (20.9173,

21.4507)

(0.7194,
0.7179)

Table 2. Summary of characteristics the Italy dataset
As shown in Table 2, are characteristics of Italy dataset. The dataset has 2 clusters; cluster 1
contains 566 records and cluster 2 has 3944 records. Based on the two dimensions, the centre
of cluster 1: Temperature1=20.9173 and Temperature2=21.4507; likewise for cluster 2:
Temperature1=0.7194 and Temperature2=0.7179.

3.1 Analysis using type-1 FLS
The dataset was pre-processed using the Weka to check for any missing values. After that,
the data was used as an input for the method (i.e. Type-1 Fuzzy Logic System).
In Figure 5, blue lines show the release desired or the real data and red lines are obtained
from the data predicted as the outcome of the method (Type-1 FLS).

Fig. 5. Type-1 FLS on Italy dataset