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Figure 1. Basic structure of GNP

Figure 1 Basic structure of GNP

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EXAMPLE OF DATABASE WITH MISSING DATA Table I is an example of an incomplete database which contains missing values. A; is an attribute in the database. Missing data is represented as ’m”’, a different value of 1 or 0. Most of the conventional association rule mining al- gorithms assume that databases are complete. Generally, databases are pre-processed in order to eliminate missing values or to replace them with an average or other statistical measures because the main problem in such kind of datasets is the difficulty for calculation of measures such as support, confidence and cosine.
EXAMPLE OF DATABASE WITH MISSING DATA Table I is an example of an incomplete database which contains missing values. A; is an attribute in the database. Missing data is represented as ’m”’, a different value of 1 or 0. Most of the conventional association rule mining al- gorithms assume that databases are complete. Generally, databases are pre-processed in order to eliminate missing values or to replace them with an average or other statistical measures because the main problem in such kind of datasets is the difficulty for calculation of measures such as support, confidence and cosine.
NT; : node type (Start node=0; Judgment node=1; Processing node=2) ID; : identification number; d, dj: delay time; Cj: connected node
NT; : node type (Start node=0; Judgment node=1; Processing node=2) ID; : identification number; d, dj: delay time; Cj: connected node
Figure 3. A connection of nodes in GNP for association rule mining with missing values
Figure 3. A connection of nodes in GNP for association rule mining with missing values
Figure 4. Basic structure of GNP for association rule mining
Figure 4. Basic structure of GNP for association rule mining
INFORMATION OF THE ORIGINAL DATASETS Table III
INFORMATION OF THE ORIGINAL DATASETS Table III
Figure 6. Number of extracted rules (generations=100) Figure 5. Number of extracted rules (min confidence=0.8)
Figure 6. Number of extracted rules (generations=100) Figure 5. Number of extracted rules (min confidence=0.8)
Figure 7. Processing Time (min confidence=0.8)
Figure 7. Processing Time (min confidence=0.8)
Figure 8. Number of Rules with different min support and min confidence
Figure 8. Number of Rules with different min support and min confidence
Figure 9. Processing Time with different min support and min confidence
Figure 9. Processing Time with different min support and min confidence
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