基于阈值自适应忆阻器Hopfield神经网络的关联规则挖掘算法
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摘要
针对基于Hopfield神经网络的最大频繁项集挖掘(HNNMFI)算法存在的挖掘结果不准确的问题,提出基于电流阈值自适应忆阻器(TEAM)模型的Hopfield神经网络的改进关联规则挖掘算法。首先,使用TEAM模型设计实现突触,利用阈值忆阻器的忆阻值随方波电压连续变化的能力来设定和更新突触权值,自适应关联规则挖掘算法的输入。其次,改进原算法的能量函数以对齐标准能量函数,并用忆阻值表示权值,放大权值和偏置。最后,设计由最大频繁项集生成关联规则的算法。使用10组大小在30以内的随机事务集进行1 000次仿真实验,实验结果表明,与HNNMFI算法相比,所提算法在关联挖掘结果准确率上提高33.9个百分点以上,说明忆阻器能够有效提高Hopfield神经网络在关联规则挖掘中的结果准确率。
Aiming at the inaccurate mining results of the Maximum Frequent Itemset mining algorithm based on Hopfield Neural Network(HNNMFI), an improved association rule mining algorithm for Hopfield neural network based on current ThrEshold Adaptive Memristor(TEAM) model was proposed. Firstly, TEAM model was used to design and implement synapses whose weights were set and updated by the ability of that threshold memristor continuously changes memristance value with square-wave voltage, and the input of association rule mining algorithm was self-adapted by the neural network. Secondly, the energy function was improved to align with standard energy function, and the memristance values were used to represent the weights, then the weights and bias were amplified. Finally, an algorithm of generating association rules from the maximum frequent itemsets was designed. A total of 1 000 simulation experiments using 10 random transaction sets with size less than 30 were performed. Experimental results show that compared with HNNMFI algorithm, the proposed algorithm improves the result accuracy of association mining by more than 33.9 %, which indicates that the memristor can effectively improve the result accuracy of Hopfield neural network in association rule mining.
Aiming at the inaccurate mining results of the Maximum Frequent Itemset mining algorithm based on Hopfield Neural Network(HNNMFI), an improved association rule mining algorithm for Hopfield neural network based on current ThrEshold Adaptive Memristor(TEAM) model was proposed. Firstly, TEAM model was used to design and implement synapses whose weights were set and updated by the ability of that threshold memristor continuously changes memristance value with square-wave voltage, and the input of association rule mining algorithm was self-adapted by the neural network. Secondly, the energy function was improved to align with standard energy function, and the memristance values were used to represent the weights, then the weights and bias were amplified. Finally, an algorithm of generating association rules from the maximum frequent itemsets was designed. A total of 1 000 simulation experiments using 10 random transaction sets with size less than 30 were performed. Experimental results show that compared with HNNMFI algorithm, the proposed algorithm improves the result accuracy of association mining by more than 33.9 %, which indicates that the memristor can effectively improve the result accuracy of Hopfield neural network in association rule mining.
引文
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[24]徐开勇,龚雪容,成茂才.基于改进Apriori算法的审计日志关联规则挖掘[J].计算机应用,2016,36(7):1847-1851.(XU KY,GONG X R,CHENG M C.Audit log association rule mining based on improved apriori algorithm[J].Journal of Computer Applications,2016,36(7):1847-1851.)
[2]STRUKOV D B,SNIDER G S,STEWART D R,et al.The missing memristor found[J].Nature,2008,453(7191):80-83.
[3]DU C,CAI F,ZIDAN M A,et al.Reservoir computing using dynamic memristors for temporal information processing[J].Nature Communications,2017,8:Article No.2204.
[4]JO S H,CHANG T,EBONG I,et al.Nanoscale memristor device as synapse in neuromorphic systems[J].Nano Letters,2010,10(4):1297-1301.
[5]KVATINSKY S,FRIEDMAN E G,KOLODNY A,et al.TEAM:ThrEshold Adaptive Memristor model[J].IEEE Transactions on Circuits and Systems I:Regular Papers,2013,60(1):211-221.
[6]江之源.基于阈值自适应忆阻模型的分析及应用研究[D].重庆:西南大学,2016:11-12.(JIANG Z Y.Analysis and application research based on threshold adaptive memristor model[D].Chongqing:Southwestern University,2016:11-12.)
[7]代祥光.忆阻神经网络的应用[D].重庆:重庆大学,2012:1-3.(DAI X G.Application of memristive neural network[D].Chongqing:Chongqing University,2012:1-3.)
[8]ALIBART F,ZAMANIDOOST E,STRUKOV D B.Pattern classification by memristive crossbar circuits using ex situ and in situ training[J].Nature Communications,2013,4(3):131-140.
[9]PARK S,SHERI A,KIM J,et al.Neuromorphic speech systems using advanced ReRAM-based synapse[C]//Proceedings of 2013IEEE International Electron Devices Meeting.Piscataway,NJ:IEEE,2013:25.6.1-25.6.4.
[10]ZIEGLER M,SONI R,PATELCZYK T,et al.An electronic version of Pavlov's dog[J].Advanced Functional Materials,2012,22(13):2744-2749.
[11]HU S G,LIU Y,LIU Z,et al.Associative memory realized by a reconfigurable memristive Hopfield neural network[J].Nature Communications,2015,6:Article number 7522.
[12]HOPFIELD J J.Neural networks and physical systems with emergent collective computational abilities[J].Proceedings of the National Academy of Sciences of the United States of America,1982,79(8):2554-2558.
[13]HOPFIELD J J,TANK D W.Neuralcomputation of decisions in optimization problems[J].Biological Cybernetics,1985,52(3):141-145.
[14]AGRAWAL R,IMIELINSKI T,SWAMI A N.Mining association rules between sets of items in large databases[C]//Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data.New York:ACM,1993:207-216.
[15]AGRAWAL R,SRIKANT R.Fast algorithms for mining association rules in large databases[C]//VLDB'94:Proceedings of the20th International Conference on Very Large Data Bases.San Francisco,CA:Morgan Kaufmann,1994:487-499.
[16]王爱平,王占凤,陶嗣干,等.数据挖掘中常用关联规则挖掘算法[J].计算机技术与发展,2010,20(4):105-108.(WANG AP,WANG Z F,TAO S G,et al.Common algorithms of association rules mining in data mining[J].Computer Technology and Development,2010,20(4):105-108.)
[17]PARK J S,CHEN M S,YU P S.An effective hash-based algorithm for mining association rules[J].ACM SIGMOD Record,1995,24(2):175-186.
[18]JI C R,DENG Z H.Mining frequent ordered patterns without candidate generation[C]//Proceedings of the 4th IEEE International Conference on Fuzzy Systems and Knowledge Discovery.Piscataway,NJ:IEEE,2007,1:402-406.
[19]SAVASERE A,OMIECINSKI E,NAVATHE S B,et al.An efficient algorithm for mining association rules in large databases[C]//VLDB'95:Proceedings of the 21st International Conference on Very Large Data Bases.San Francisco,CA:Morgan Kaufmann,1995:432-444.
[20]TOIVONEN H.Sampling large databases for association rules[C]//VLDB'96:Proceedings of the 22th International Conference on Very Large Data Bases.San Francisco,CA:Morgan Kaufmann,1996:134-145.
[21]AGRAWAL R,SHAFER J C.Parallel mining of association rules[J].IEEE Transactions on Knowledge and Data Engineering,2007,8(6):962-969.
[22]GABER K,BAHI J M,EL-GHAZAWI T A.Parallel mining of association rules with a Hopfield type neural network[C]//ICTAI'00:Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence.Washington,DC:IEEE Computer Society,2000:90-93.
[23]ABDALLA H,PICKETT M D.SPICE modeling of memristors[C]//Proceedings of the 2011 IEEE International Symposium on Circuits and Systems.Piscataway,NJ:IEEE,2011:1832-1835.
[24]徐开勇,龚雪容,成茂才.基于改进Apriori算法的审计日志关联规则挖掘[J].计算机应用,2016,36(7):1847-1851.(XU KY,GONG X R,CHENG M C.Audit log association rule mining based on improved apriori algorithm[J].Journal of Computer Applications,2016,36(7):1847-1851.)