# Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis
## 3 Related Work
### Association Rule and Its Algorithm  
Searching for hidden relationships between items from large-scale datasets is called association analysis. The key research approaches of association analysis include FIM and association rules. On one hand, FIM is a collection of items that often appear together. Given the past superiority, FIM has an extensive applicational field from the latest researches, such as extracting useful knowledge from event logs [29], using sliding windows capable of extracting tendencies from continuous data flows [30], directing membrane separator development for microbial fuel cells [31], assisting healthcare researchers in identifying medications and medication combinations that associate with a higher risk of acute kidney injury (AKI) using electronic medical records (EMRs) [32], protecting the cloud servers' privacy of datasets from frequency analysis attack [33], and applying the FP-Growth algorithm in determining frequent itemsets in association with data mining to find customer spending habits in buying goods simultaneously [34]. On the other hand, association rules analyze the possible strong relationship between two items. In particular, both Apriori and FP-Growth are algorithms used in itemset of association rule mining in FIM and a large amount of transaction data. The market basket analysis is a typical task for the association rules of mining, and it is the most widely used method, developed by [PERSON] [8] and others at the IBM Almaden research center to identify relationships in a large amount of data. The advantage is that it can easily explain the generated rules and completely show the cross-effect of variables. However, the selected rules and conditions or set are very important; that is, they may produce cumbersome and messy results if the criteria is set slightly loose, or a relatively small number of variables may be ignored with unparsed analysis result being produced. The most common analysis data are represented by the transaction database, as it attempts to identify the potential association rules from the seemingly relevant, but not identical, customer transaction records, among which the most well-known example is the association rule, which was used to analyze the relationship between beer and diapers in American hypermarkets. \begin{table}
\begin{tabular}{c c c} \hline
**Data Preparation Steps** & **Cause** & **Problem** \\ \hline \multirow{4}{*}{Data integration} & Differences after the integration of different source data & Inconsistent data \\  & Data or fields with the same meaning appear & Redundant data \\  & The value of data is out of the reasonable range & Incorrect data \\  & Field or value of a duplicate record & Duplicate data \\  & Characteristics of data, improper measurement, or data input error & Outliers \\  & Data omission caused by measuring equipment or human factors & Missing value \\  & Error of data or deviation of data input & Noise \\  & The data format does not meet the assumptions of mining tool & Inappropriate data scale \\  & Data reduction & Data or dimension too high & Too much data \\ \hline \end{tabular}
\end{table} 
Table 1: Data integration and cleaning. The algorithm of association rule is composed of the search data method, calculated items, and support degree, and the features of the association rule algorithm are described in Table 2 below. 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.