Association rule mining using binary particle swarm optimization

2013 ◽  
Vol 26 (8) ◽  
pp. 1832-1840 ◽  
Author(s):  
K.N.V.D. Sarath ◽  
Vadlamani Ravi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Rule Mining ◽  
Swarm Optimization

scholarly journals Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zhicong Kou ◽  
Lifeng Xi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Binary Particle Swarm Optimization ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Product Features

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

Fuzzy Multi-Objective Association Rule Mining Using Evolutionary Computation

2017 ◽  
pp. 119-148
Author(s):  
Ganghishetti Pradeep ◽  
Vadlamani Ravi
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Threshold Accepting ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Association Rule Miner

In this chapter, we model association rule mining as a Fuzzy multi-objective global optimization problem by considering several measures of strength such as support, confidence, coverage, comprehensibility, leverage, interestingness, lift and conviction by utilizing various fuzzy aggregator operators. In this, pdel, each measure has its own level of significance. Three fuzzy multi-objective association rule mining techniques viz., Fuzzy Multi-objective Binary Particle Swarm Optimization based association rule miner (FMO-BPSO), a hybridized Fuzzy Multi-objective Binary Firefly Optimization and Threshold Accepting based association rule miner (FMO-BFFOTA), hybridized Fuzzy Multi-objective Binary Particle Swarm Optimization and Threshold Accepting based association rule miner (FMO-BPSOTA) have been proposed. These three algorithms have been tested on various datasets such as book, food, bank, grocery, click stream and bakery datasets along with three fuzzy aggregate operators. From these experiments, we can conclude that Fuzzy-And outperforms all the other operators.

Privacy Preserving Fuzzy Association Rule Mining in Data Clusters Using Particle Swarm Optimization

2021 ◽  
pp. 1096-1116
Author(s):  
Sathiyapriya Krishnamoorthy ◽  
G. Sudha Sadasivam ◽  
M. Rajalakshmi ◽  
K. Kowsalyaa ◽  
M. Dhivya
Keyword(s):  
Particle Swarm Optimization ◽  
Side Effects ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Fuzzy Association Rule ◽  
Sensitive Knowledge

An association rule is classified as sensitive if its thread of revelation is above certain confidence value. If these sensitive rules were revealed to the public, it is possible to deduce sensitive knowledge from the published data and offers benefit for the business competitors. Earlier studies in privacy preserving association rule mining focus on binary data and has more side effects. But in practical applications the transactions contain the purchased quantities of the items. Hence preserving privacy of quantitative data is essential. The main goal of the proposed system is to hide a group of interesting patterns which contains sensitive knowledge such that modifications have minimum side effects like lost rules, ghost rules, and number of modifications. The proposed system applies Particle Swarm Optimization to a few clusters of particles thus reducing the number of modification. Experimental results demonstrate that the proposed approach is efficient in terms of lost rules, number of modifications, hiding failure with complete avoidance of ghost rules.

scholarly journals Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

2019 ◽  
Vol 9 (2) ◽  
pp. 1359
Author(s):  
Imam Tahyudin ◽  
Hidetaka Nambo
Keyword(s):  
Particle Swarm Optimization ◽  
Association Rule ◽  
Association Rule Mining ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Cauchy Distribution ◽  
Objective Functions ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.</p></div></div></div>

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