Book Chapter10.1007/978-981-16-1843-7_41
BPSO Algorithm with Opposition-Based Learning Method for Association Rule Mining
Qianyi Zhong,Qian Qian,Yong Feng,Yunfa Fu +3 more
- 01 Jan 2021
- pp 351-358
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TL;DR: In this paper, a binary particle swarm optimization algorithm is proposed to improve the association rule mining problem, which does not need to manually specify support and confidence thresholds and uses an opposition-based learning method to reduce the probability of the algorithm falling into local extreme.
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Abstract: Traditional association rule mining algorithm does not operate efficiently when processing large and high-dimensional data. With an opposition-based learning method, a binary particle swarm optimization algorithm is proposed to improve the association rule mining problem. The proposed method uses a binary particle swarm algorithm to search for association rules and does not need to manually specify support and confidence thresholds. In addition, opposition-based learning is introduced, and the primary and the secondary opposition-based learning methods are used to reduce the probability of the algorithm falling into local extreme and improve the convergence accuracy of the algorithm. The experimental results show that the improved algorithm converges faster than existing algorithms and balances multiple indexes of reliability, correlation, and comprehensibility, thus mining more effective association rules.
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Citations
A Survey on Particle Swarm Optimization for Association Rule Mining
TL;DR: The applications of PSO-based ARM algorithms are discussed, the current status of the improvement in PSO algorithms is discussed in stages, and further research directions are proposed by exploring the existing problems.
References
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Rakesh Agrawal,Tomasz Imielinski,Arun N. Swami +2 more
- 01 Jun 1993
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
A discrete binary version of the particle swarm algorithm
James Kennedy,Russell C. Eberhart +1 more
- 12 Oct 1997
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
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Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.
4.5K
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
TL;DR: A novel frequent-pattern tree (FP-tree) structure is proposed, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and an efficient FP-tree-based mining method, FP-growth, is developed for mining the complete set of frequent patterns by pattern fragment growth.
Opposition-Based Learning: A New Scheme for Machine Intelligence
Hamid R. Tizhoosh
- 28 Nov 2005
TL;DR: Opposition-based learning as a new scheme for machine intelligence is introduced and possibilities for extensions of existing learning algorithms are discussed.
2K