TKFIM: Top-K frequent itemset mining technique based on equivalence classes

Abstract

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts

“…The Top-K Miner finds all topmost FIs as per the tuned parameter, but suffers as memory-expensive due to the Candidate Itemsets Search tree (CIS-tree). Moreover, Iqbal et al presented the TKFIM algorithm which inherits the Apriori mining technique for the discovery of K-topmost FIs [ 26 ]. This algorithm performs excessive candidate generation because it uses common itemsets prefixes of the already produced topmost frequent itemsets.…”

“…To analyze the performance trends of the , it was compared against two recent top-most IFIs mining techniques, top-K Miner [ 29 ] and TKFIM [ 26 ]. The top-K Miner uses depth-first traversal and is not an Apriori-inspired method, while the TKFIM method uses breadth-first traversal strategy, which is considered an Apriori-inspired approach.…”

“…For experimental evaluation with TKIFIs Miner, we selected three frequent itemsets mining methods: FP-Growth [ 40 ], Top-K Miner [ 29 ], and TKFIM [ 26 ]. The FP-Growth method is used as a benchmark method for experimental evaluation of the many mining methods.…”

“…Many algorithms have been proposed in the area of mining the topmost frequent itemsets [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. These algorithms are commonly named K-topmost or topmost FIM approaches.…”

“…In general, the topmost FIs-mining techniques designed based on the aforementioned principles are computationally inefficient [ 30 ]. Recent techniques for topmost patterns mining include the mining top-K frequent patterns without support threshold proposed by Salam et al [ 30 ], the top-K identical frequent without support threshold by Rehman et al [ 29 ], and the top-K frequent itemsets mining technique based on equivalence classes by Iqbal et al [ 26 ]. All Apriori-and-FP-growth independent top-K methods mentioned earlier are not viable to perform due to the two main limitations described below.…”