## 2 Literature Review
The TKRIMPE works in two phases. In the first phase, the dataset is partitioned, and each partition is scanned to collect the support, regularity, and TIDs of a single itemset. The itemsets are ranked in the top-K list. The second phase is reserved for the itemset mining purpose. This phase is performed using a best-fit strategy over itemsets available in the top-K list. The itemsets with highest support and common prefixes are considered first for candidate formation. Once the candidate is formed, the support estimation method is applied to determine its estimated support. If the candidate itemset has lower estimated support than the least support of the itemset in the top-K list, it is dropped from the search space. The top-K frequent regular pattern discovery methods avoid a large number of patterns. However, these methods still produce a result set containing redundant patterns. To avoid the redundancy in the result set, top-K frequent regular closed patterns method with minimum length constraint is proposed by [PERSON] et al. [21], namely TFRC-Mine. The TFRC-Mine follows a two-step process, as with TKRIMPE. The first step is to scan and initialize the top-K list and the second step is to use best-fit strategy for search space exploration and pattern mining. For support calculation partition, a dynamic bit vector approach is applied in TFRC-Mine. The TFRC-Mine method is found to be effective to prune the candidate itemsets and to count support of the patterns. The TFRC-Mine is reported to be more computationally efficient than MTKPP [2], TR-CT [12], and TKRIMPE [19] for large value of K over dense datasets. [PERSON] et al. [22] has proposed periodic frequent-equivalence class transformation (PF-ECLAT) for periodic frequent itemset mining. The PF-ECLAT uses frequent itemset mining ECLAT (equivalence class clustering and bottom-up lattice traversal) algorithm for periodic frequent itemset mining. The PF-ECLAT requires minimum support threshold and maximum periodicity as parameter in advance from users. Firstly, the PF-ECLAT finds one-length patterns and converts the given dataset into a vertical format by applying the PFP-List data structure. Secondly, the PF-ECLAT works on the PFP-List in depth-first order and applies a down word closure property to effectively prune itemsets from the search space. The PF-ECLAT algorithm is found to be more computationally and memory-usage efficient than other periodic frequent itemset mining algorithms. However, the PF-ECLAT has the limitations of requiring a support threshold and application on the offline datasets. [PERSON] et al. [20] has introduced a single-pass incremental miner of top-k frequent-regular itemset (IMTFRI). The algorithm is described in two main steps. In the first step, the incoming batch of transaction is read one by one to record the frequency information of items. In the second step, the entire top-K frequent regular items list is computed using the frequency information of the items in the first step. The IMTFRI approach increments the frequency counts of already discovered top-K frequent regular items. The new top-K frequent regular itemsets are also discovered from the updated dataset chunk. For the frequency counting, it uses a partition dynamic bit vector (PDBV) data structure. This approach uses breadth-first best-fit traversal to mine top-K frequent regular itemsets. No pruning mechanism is utilized during traversal to prune the itemsets whose frequency is decreasing due to the baseline frequency updation mechanism. In other words, IMTFRI uses only single criteria to merge itemsets whose prefixes are the same rather than also considering their frequency, which could drop due to baseline frequency updation. [PERSON] et al. [15] has proposed a top-K frequent itemset mining (TKFIM) algorithm for detecting FIs without a user-defined support threshold. The TKFIM uses the idea of equivalence classes of set theory where each class represents an independent group of itemsets.