# Optimization of Associative Knowledge Graph Using TF-IDF Based Ranking Score
## 4 Experimental Results
Therefore, the proposed algorithm generates association rules faster than a conventional association rule algorithm. Figure 11: Word Count and Generation Speed according to TF-IDF and Min Support. In the second performance evaluation, the Apriori algorithm is compared with the FP-tree algorithm, with the uses of support, confidence, and lift. Apriori algorithm generates association rules for words in all transactions, and does pruning with the use of support and confidence. It has low performance in terms of rule generation speed. To improve the disadvantage, FP-Tree is used. FP-Tree utilizes a linked list to generate a frequency item pattern. By mining the frequency pattern, the algorithm can achieve efficient expansion and has a faster search speed than the Apriori algorithm [42]. Therefore, in terms of association rule generation speed and usefulness, a conventional FP-tree association rule algorithm, Apriori algorithm, and the improved Apriori association rule algorithm proposed in this study are compared. In comparison, the number of generated rules is limited, and each algorithm calculates the average values of Support, Confidence, and Lift [43,44]. The number of rules is limited to 500, 1000, 1500, 2000, and 2500, respectively, and the performance of each algorithm is evaluated. Figure 13 shows rule generation speed comparison of FP-Tree, Apriori, Apriori_TF-IDF algorithm. The results from the comparison between the FP-tree association rule algorithm, the Apriori algorithm, and the improved Apriori association rule (Apriori_TF-IDF) algorithm in terms of rule generation speed. The x-axis represents the number of rules, and the y-axis represents a generation speed. As shown in Figure 13, the proposed Apriori-TF-IDF algorithm generates rules 0.4-0.8 seconds faster than other algorithms. For the objective evaluation, support, confidence, and lift are applied. Table 7 shows average support, confidence value of association algorithms. Figure 12: Word Count and Generation Speed according to TF-IDF and Confidence. Figure 13: Rule Generation Speed Comparison of FP-Tree, Apriori, Apriori_TF-IDF Algorithm. As shown in Table 7, FP-Tree and Apriori algorithms that do not use TF-IDF Ranking Scores have higher average values of support than the algorithm that uses the ranking. Regarding a value of confidence, Apriori with no use of ranking has the highest score, followed by Apriori with the use of top 20% TF-IDF Ranking, FP-Tree with no use of ranking, and FP-Tree with the use of top 20% TF-IDF Ranking in order. Nevertheless, judging the consistency of rules with the uses of Support and Confidence is limited. For example, if the association rule [Beverage] -> [Coke] is extracted on the basis of the terms Beverage and Coke, there is a possibility that the rule is a common-sense rule, rather than unpredicted new information. From the perspective of usefulness, there is no positive result. Therefore, the usefulness of rules is evaluated with the use of the lift. Table 8 shows average lift value of association algorithms. Figure 14 shows the average lift score of association algorithms by number of rules.