# Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis
## 3 Related Work
### Association Rule and Its Algorithm
FP-Growth: FP-Growth was mentioned in the introduction of the prior algorithm, which needs more time and space complexity, since it searches all of the data many times before producing frequent subsets and generates a large number of candidate subsets at the same time; this is why the efficiency of the prior algorithm is low when it faces huge datasets. Important parameters of the association rule: There are three important parameters in the association rule, namely, support, confidence, and lift. The first parameter, support, represents the frequency of an item appearing in all shopping carts. If the relationship degree of two commodities A and B is analyzed, it represents the ratio of these two items in all transaction records. The greater the relationship, the more important the association rule index is. The formula is as follows: support (A\(\Rightarrow\)B) = P(A\(\cap\)B)  
The second parameter is confidence. Taking the shopping basket as an example, the conditional probability of purchasing commodity A and commodity B can be used to measure the accuracy of the association rule. The formula is as follows: confidence (A\(\Rightarrow\)B) = P(A\(\cap\)B)/P(A)  
The third parameter is lift, which is used to measure the impact of the association rule on commodity frequency. Only association rules with a gain greater than 1 make sense. The formula is as follows: lift (A\(\Rightarrow\)B) = P(A\(\mid\)B)/P(B)  
It can be seen from the calculation method that when the lift value is greater than 1, there is a positive relationship between A and B; however, some association rules are useless that can be erased when its lift value is insufficient. How the minimum degree of support and confidence for the data to be analyzed are set is the key to identifying the association rules. In this study, FP-Growth is used to calculate association rules. With this method, a set of tree structures is constructed, and it only needs to scan the database twice in the mining process so as to reduce a lot of I/O time. The minimum degrees of confidence and support are set as 90 and 10, respectively, to ensure that the association rules generated have a positive relationship. The main steps are divided into two stages: (1) establishing the FP tree; (2) mining the FP tree. \begin{table} \begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Main Features** & **Disadvantage** & **Search Method** & **Data Configuration** \\ \hline \multirow{4}{*}{**Apriori**} & The candidate itemsets are & & Database needs to be & \\  & generated repeatedly to identify & searched repeatedly, and I/O & & \\  & all of the high-frequency ones, & time needs to be spent. & & \\  & and the rules are then deduced. & FP-Growth is the deductive basis & & \\  & of the algorithm, and it can deal & More processing time and & \\  & with a large amount of data & storage space are needed to & \\  & effectively, thereby surpassing & store FP trees. & & \\  & the limitations of Apriori. & & & \\ \hline \hline \end{tabular}   
* Apriori: The Apriori algorithm is one of the classic algorithms of the association rule in the field of data mining, and it was put forward by [PERSON] and [PERSON] [PERSON] and [PERSON] (1994) in 1994. The Apriori algorithm is used to deal with transaction information databases, such as lists of goods purchased by customers or lists of frequently visited web pages. The algorithms of other types of relational rules are more inclined to identify the relationship between data without transaction information or without a time stamp. The Apriori algorithm is used to search the dataset in the principle of “breadth first”, then calculate and analyze the candidate subsets with a tree structure. * FP-Growth: FP-Growth was mentioned in the introduction of the prior algorithm, which needs more time and space complexity, since it searches all of the data many times before producing frequent subsets and generates a large number of candidate subsets at the same time; this is why the efficiency of the prior algorithm is low when it faces huge datasets.