An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes

Abstract

The Eclat algorithm is a typical frequent pattern mining algorithm using vertical data. This study proposes an improved Eclat algorithm called ETPAM, based on the tissue-like P system with active membranes. The active membranes are used to run evolution rules, i.e., object rewriting rules, in parallel. Moreover, ETPAM utilizes subsume indices and an early pruning strategy to reduce the number of frequent pattern candidates and subsumes. The time complexity of ETPAM is decreased from O(t2) to O(t) as compared with the original Eclat algorithm through the parallelism of the P system. The experimental results using two databases indicate that ETPAM performs very well in mining frequent patterns, and the experimental results using four databases prove that ETPAM is computationally very efficient as compared with three other existing frequent pattern mining algorithms.

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

“…Various meta-heuristic algorithms, such as GA [69], DE [70] and its variants [71,72], PSO [73,74], ABC [75], and BBO [76], are usually introduced to SNS-based MIEAs as the basic evolutionary operation in the cell or neural [77][78][79][80][81]. The membrane structure in DNS-based MIEAs can be dynamically changed according to communication channel rules, and this class of MIEAs, with an extended membrane structure, has great potential for solving complex problems [82,83]. For another kind of EMC, ADMCMs are designed to overcome the complex programmability of membranebased modes, and the automated synthesis of computing models by applying various meta-heuristic methods can be obtained through this class of EMC [84][85][86].…”

A novel consumer preference mining method based on improved weclat algorithm

“…Regarding the selection of association rule mining algorithms because market data are relational data with higher density than commodity/transaction data, among the three classical association rule mining algorithms, namely, Apriori (Agrawal et al, 1993), FPgrowth (Han et al, 2000) and Eclat (Jia et al, 2019), the Eclat algorithm based on the vertical data format can process dense data sets with the highest efficiency. However, in practical applications, the Eclat algorithm is characterized by a significant problem; after introducing the transaction weights to the data set, the definition of weights will disrupt the downwardclosure property of the process when mining frequent itemsets (Sun and Bai, 2008), which will lead to an uncontrollable running time.…”