Association rule mining using swarm intelligence and domain ontology

2012 ◽  
Author(s):  
M Nandhini ◽  
M Janani ◽  
S.N Sivanandham
Keyword(s):  
Swarm Intelligence ◽  
Association Rule ◽  
Association Rule Mining ◽  
Domain Ontology ◽  
Rule Mining

scholarly journals Shared Variable Extraction and Hardware Implementation for Nonlinear Boolean Functions Based on Swarm Intelligence

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Longmei Nan ◽  
Xiaoyang Zeng ◽  
Yiran Du ◽  
Zibin Dai ◽  
Lin Chen
Keyword(s):  
Swarm Intelligence ◽  
Association Rule ◽  
Boolean Functions ◽  
Association Rule Mining ◽  
Operation Time ◽  
Lookup Table ◽  
Coarse Grained ◽  
Rule Mining ◽  
Shared Variable ◽  
Shared Variables

To solve the problem of complex relationships among variables and the difficulty of extracting shared variables from nonlinear Boolean functions (NLBFs), an association logic model of variables is established using the classical Apriori rule mining algorithm and the association analysis launched during shared variable extraction (SVE). This work transforms the SVE problem into a traveling salesman problem (TSP) and proposes an SVE based on particle swarm optimization (SVE-PSO) method that combines the association rule mining method with swarm intelligence to improve the efficiency of SVE. Then, according to the shared variables extracted from various NLBFs, the distribution of the shared variables is created, and two corresponding hardware circuits, Element A and Element B, based on cascade lookup table (LUT) structures are proposed to process the various NLBFs. Experimental results show that the performance of SVE via SVE-PSO method is significantly more efficient than the classical association rule mining algorithms. The ratio of the rules is 80.41%, but the operation time is only 21.47% when compared to the Apriori method, which uses 200 iterations. In addition, the area utilizations of Element A and Element B expended by the NLBFs via different parallelisms are measured and compared with other methods. The results show that the integrative performances of Element A and Element B are significantly better than those of other methods. The proposed SVE-PSO method and two cascade LUT-structure circuits can be widely used in coarse-grained reconfigurable cryptogrammic processors, or in application-specific instruction-set cryptogrammic processors, to advance the performance of NLBF processing and mapping.

GPU-based swarm intelligence for Association Rule Mining in big databases

2019 ◽  
Vol 23 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Youcef Djenouri ◽  
Philippe Fournier-Viger ◽  
Jerry Chun-Wei Lin ◽  
Djamel Djenouri ◽  
Asma Belhadi
Keyword(s):  
Swarm Intelligence ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

Research on Text Conceptual Relation Extraction Based on Domain Ontology

2013 ◽  
Vol 739 ◽  
pp. 574-579
Author(s):  
Dao Wang Li
Keyword(s):  
Semantic Similarity ◽  
Association Rule ◽  
Association Rule Mining ◽  
Relation Extraction ◽  
Extraction Methods ◽  
Domain Ontology ◽  
Ontology Learning ◽  
Rule Mining ◽  
Conceptual Relation ◽  
Learning Research

At present, the ontology learning research focuses on the concept and relation extraction; the traditional extraction methods ignore the influence of the semantic factors on the extraction results, and lack of the accurate extraction of the relations among concepts. According to this problem, in this paper, the association rule is combined with the semantic similarity, and the improved comprehensive semantic similarity is applied into the relation extraction through the association rule mining relation. The experiments show that the relation extraction based on this method effectively improves the precision of the extraction results.

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