An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors

Liu, Xiyu; Zhao, Yuzhen; Sun, Minghe

doi:10.1155/2017/6978146

Cited by 26 publications

(21 citation statements)

References 19 publications

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“…Vertical data is more efficient than horizontal data in the process of obtaining the support of items because an algorithm only needs to read the columns related to a query, but does not need to read other unnecessary columns. For instance, if the support of itemset {I 1 I 2 } is needed in Table 1, an algorithm just needs to read and intersect the TID_sets of I 1 and I 2 and find support (I 1 I 2 ) = Num[ (1,4,5,7,8,9)∩ (1,2,3,4,6,8,9)] = Num(1, 4, 8, 9) = 4, instead of scanning the entire database as using horizontal data. Table 1.…”

Section: The Eclat Algorithmmentioning

confidence: 99%

“…To verify the efficiency of the two improvements introduced into the original Eclat algorithm, ETPAM with rules executed serially is used to compare with those of Apriori [7], Fp-growth [24], and the original Eclat algorithm [25]. The total running time is used as a metric to evaluate performance in experiments.…”

Section: Efficiency Of the Proposed Algorithmmentioning

confidence: 99%

“…Object sets are usually represented by a string of symbols and evolution rules of the objects are given in the form of rewriting rules. A P system is a distributed and parallel computing model, and evolutionary rules run synchronously, non-deterministically, and in maximum parallel [7], making the system computationally very efficient.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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.

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Section: The Eclat Algorithmmentioning

confidence: 99%

Section: Efficiency Of the Proposed Algorithmmentioning

confidence: 99%

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

2019

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“…The effectiveness and feasibility of the proposed method in ARM has been proved. Liu et al [12] proposed a fast Apriori algorithm, called ECTPPI-Apriori, for processing large datasets. The algorithm uses a parallel mechanism in the ECPI tissue-like P system.…”

Section: Literature Reviewmentioning

confidence: 99%

Fast Exhaustive Search Algorithm for Discovering Relevant Association Rules

Amraoui¹,

Mhamdi²,

Elloumi³

2019

International Conferences on Software Engineering and Knowledge Engineering

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Association Rules Mining (ARM) is one of the most important tasks of Data mining. The purpose of ARM is to discover relationships having an interest between attributes/patterns stored in very large databases. Nowadays several efficient algorithms have been developed by the researchers for the discovery of relevant Association Rules (ARs). These latter are responsible for decision making in several domains, such as medicine, finance, marketing and many other fields. In this paper, we propose a new algorithm based on exhaustive search to find relevant AR to make the decision and to predict the chance of occurring the Diabetes Mellitus (DM). We develop an algorithm to mine data in less time and less complexity without losing information. Finally, we test our approach using a real database to evaluate the efficiency of our algorithm compared to Apriori algorithm.

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“…Liu and Xue [31] proposed a new cluster splitting technique based on Hopfield neural networks and P systems. Liu et al [32] presented an improved Apriori algorithm, named ECTPPT-Apriori, based on evolution-communication tissue-like P systems with promoters and inhibitors. Peng et al [33] designed a tissue-like membrane system with a fully connected structure using an inherent mechanism to deal with automatic clustering problems.…”

Section: Introductionmentioning

confidence: 99%

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Wang

Liu

Sun

et al. 2020

Mathematical Problems in Engineering

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An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. e purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. is extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

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An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors

Cited by 26 publications

References 19 publications

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

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

Fast Exhaustive Search Algorithm for Discovering Relevant Association Rules

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

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