# Performance Evaluation of Sequential Rule Mining Algorithms
## 2 Literature Review  
Numerous research has been suggested to enhance the mining process of sequential patterns. The primary obstacle in mining sequential patterns is producing unessential sequential patterns when setting the support measure with a very low value. Mining the sequential rules is an alternative to sequential patterns that assist the users in having knowledge of sequence items for making a prediction. It has been found in numerous zones like electronic learning [5], manufacturing simulation [6], the analysis of customer behavior [7], and decision systems [8].  
The primary algorithm proposed for mining the sequential rules by [PERSON] and [PERSON] is studying the sequence behavior for the prediction process. It helps in finding all items that occurred as often as possible in a sequence dataset [9]. Then, most research has been proposed to produce sequential patterns that appear frequently and remove repetitive rules within the following stage of the mining process. They had to check the database numerous times to find the support of each itemset that induces minimum complexity and extra cost like RuleGen algorithm [10]. After that, the researchers have found rules with no consideration of their arrangement; refer to partially ordered sequential rules (POSR). It stands up on those items in the predecessor, and forerunner sides are unarranged. Two primary algorithms for the POSR are named CMRule and CMDeo [11; 12]. The CMRule is the baseline algorithm that expels the temporal information and generates rules that accomplish the support threshold. It relies on the produced number of sequential rules that cause ineffectual performance [13]. The second baseline algorithm is CMDeo, which acts as more proficient than the CMRule. It discovers all substantial rules size \(1*1\) by expanding both sides of the rules. To address the previous obstacles, RuleGrowth has been proposed. It can extend rules amid guarantees as it was necessary for rules in sequence datasets [14; 15; 16].  
Various algorithms have been developed based on the prefix tree to realize superior efficiency. The CNR, IMSR, and MNSR algorithms rely on enhancing the rules by eliminating non-redundant rules [17; 18; 19]. They sort the sequences according to the support value ascendancy before producing rules. In this way, it diminishes the scan's number and minimizes complexity to \(o\left(n^{2}\right)\).  
TRuleGrowth is an extension of RuleGrowth. It has been developed to control the maximum consecutive items by applying an additional constraint called window size. This constraint helps to produce much fewer number of rules. So, it enhances the performance due to diminishing the request space used to store the generated rules [20; 21; 22; 23].  
The researchers proposed the Top-k sequential rule mining to overcome the problem of the difficulty of determining the valid minSup value that suits each database features [24; 25]. It depends on two parameters: k is the number of rules that users want to generate, and the minConf that is easy to determine due to user satisfaction. The TopSeqRules is the first algorithm that addresses the Top-k sequential rule mining. It depends on the same strategy as the RuleGrowth algorithm. It integrates the two procedures to expand the left and the right sides of the rule with the general procedure for mining the top-k pattern. Researchers enhanced many algorithms to reduce disk space and generate interesting sequential rules. The Top-k non-redundant sequential rule (TNS) utilizes the TopSeqRules for mining the top-k to eliminate the redundancy on the generated rules [26; 27].  
A proficient algorithm named non-redundant with dynamic bit-vector has been suggested. It is used to remove unnecessary rules early through utilizing dynamic bit-vector with pruning techniques, so that it improves the performance by reducing the execution time and memory utilization [28; 29; 30].