# Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis
## 1 Introduction  
At present, the hard disk drive (HDD) [1] and solid-state drive (SSD) [2] are the main storage devices in the computer industry. The structure of the HDD allows one to access data through the read-write mode between the magnetic read-write head and the disk and, then, store the data on the magnetic material on the disk. The surface of such disks is easily scratched by shaking and can result in functional damage. The SSD, also known as the electronic hard disk, can reduce the damage of components caused by shaking due to its structure of electronic components, but there are still cases of data loss caused by error correction in not-and (NAND) [3] flash memory damage. Unlike the HDD, the SSD mainly stores data by the flow of electrons in NAND flash memory, which is also the key to the SSD being able to read and write faster than the HDD. Furthermore, the SSD also appears to be gradually replacing the HDD, so how to manage the quality of the SSD and how to serve customers are becoming important management issues.  
In order to improve the quality and storage capacity, SSD manufacturers continue to develop new types of memory, such as resistive random-access memory (RRAM) [4], parallel random-access memory (PRAM), and static random-access memory (SRAM) [5]. In addition, currently, the SSD in the industry still uses NAND flash memory. The development of R&D capability has enabled the change from the original single-level cell (SLC) and multi-level cell (MLC) to the mainstream trinary-level cell (TLC) and the next-generation qual-level cell (QLC) [6] so as to meet the market price requirements. The goals regarding the process improvement of NAND flash memory are to reduce the overall cost of the SSD and increase the storage capacity, but the limitation of the \"program and erase\" cycle (PEcycle) [7] on NAND flash memory means that no effective improvement can be achieved; thus, the durability of the SSD remains questionable.  
Apriori is an algorithm for frequent itemset mining (FIM) and association rule mining of itemsets in mass transactional data [8]. The frequent pattern growth (FP-Growth) is an efficient, scalable, convenient, and improved algorithm of the Apriori algorithms for the mining of a complete set of frequent patterns (FP) by using an extended prefix-tree structure that allows compressed and crucial information about frequent patterns to be stored. The FP-Growth algorithm [9; 10; 11; 12] presents a unique way to identify frequent itemsets without using candidate generations. Some current data mining applications, such as web usage mining and promoting education, are implemented by the FP-Growth algorithm, thus improving performance [13; 14].  
This study collects and analyzes the data of SSD maintenance around the world through computer company A and uses data mining technology and algorithms to identify the data rules of SSD error so as to improve the quality and service strategy of customer service personnel. The goals of this study are as follows: (1) identification of the data rules of SSD error; (2) quality improvement; (3) determination of the direction of customer service strategy; (4) optimization of \"parts management\" and \"supply chain distribution\".