# RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities
## 2 Background  
The frequent patterns present a considerable advantage for performing other data mining activity processes, e.g., clustering, association-rule mining, classification, etc. In the work by [PERSON] et al. [7; 8], one can find frequent items from a large database. Discovering itemset/ frequent patterns is an important process for performing other data mining activity processes such as association rule mining, classification, prediction, clustering [7; 8]. These approaches are called Apriori-like algorithms and pattern growth methods; the apriori-like algorithms follow a basic property like downward closing or anti-monotone, which means that any subset of the frequent itemsets is also frequent. This property is used to eliminate the non-frequent itemset with the given support or threshold support values. The presented approach assumes that all items are binary variables and considers whether they are consumed or not. Not many researchers have proposed an extension of the itemset mining, called itemset mining, with quantities that consider the consumption of the item along with the quantity [9]. Other researchers have investigated the weighted itemset mining considering the occurrence of the item along with its weight or importance [10]. In the work of [PERSON] et al. [11], the authors have introduced different algorithms that describe sequential pattern mining problems for determination of the frequent sequences of the items which should qualify a user-specified minimum support. Then, [PERSON] and [PERSON] [12] proposed a novel methodology for ranking the association rules and introduced an algorithm for extracting the best rules using rule support and confidence from the large datasets. In Reference [13], the authors developed a new algorithm called PrefixSpan where the global database is projected into a set of smaller (local) databases and sequential patterns that are constructed by exploring frequently occurring datasets of the local databases. In the recent scientific research, the sequential pattern mining technique is extensively used by researchers in various directions, such as incremental sequence mining, biological sequence mining, multi-dimensional sequence pattern mining, and approximate sequence mining in noisy environments [14]. For example, when a customer buys a computer, he/she is likely to buy a printer, and then a storage disk at a later point in time. For this retail information, managers can do suitable shelf replacements and promotional activities. Several works proposed different algorithms for mining sequential patterns from a large database. The problem of performance is the main issue for sequential pattern mining. Thus, over time, researchers have improved performance by adding some constraints to the mining process. There are many works which contribute to constrained sequential mining. In the work by [PERSON] et al. [15], the author used weight constraints to reduce the number of unimportant patterns. In addition, in Reference [16], the authors proposed a mining model by incorporating user-defined constraints for discovering the knowledge that would satisfy the user's needs in a better way. In Reference [17], a Generalized Sequential Models (GSP) algorithm was used for discovering sequential patterns with time constraints. In addition, a new algorithm called the Graph Time Constraints (GTC) algorithm [17] was proposed for mining patterns in large databases. In Reference [18], a new methodology for extracting weighted sequential patterns by considering the time interval weight was developed. Many research works have applied data mining techniques to achieve various objectives such as detecting faults, predicting failure probabilities, predicting maintenance intervals, prioritizing equipment, determining fault trends, identifying the cause of failures, etc. In Reference [19], the author presented a text mining method by using the abstract and keywords of 150 research papers and revealed that only 8% of data mining studies cover the area of maintenance.