基于粒子群的關(guān)聯(lián)規(guī)則挖掘算法研究
發(fā)布時間：2018-08-02 12:44
【摘要】：關(guān)聯(lián)規(guī)則分析是數(shù)據(jù)挖掘中最主要的分支,其主要目的就是為了挖掘存在于事務(wù)數(shù)據(jù)庫中隱藏的關(guān)系或者聯(lián)系。隨著大數(shù)據(jù)的普及,傳統(tǒng)的關(guān)聯(lián)規(guī)則挖掘算法暴露出的問題越來越明顯,使得算法的挖掘效率也有所下降。粒子群優(yōu)化算法作為一種群智能優(yōu)化算法的代表,近年來被廣泛應(yīng)用于不同的領(lǐng)域,其中就包括關(guān)聯(lián)規(guī)則分析方面。本文就是通過將粒子群優(yōu)化算法與關(guān)聯(lián)規(guī)則挖掘算法相結(jié)合,對關(guān)聯(lián)規(guī)則挖掘算法提出改進(jìn)思路。為了滿足關(guān)聯(lián)規(guī)則挖掘得到的規(guī)則信息能夠隨著時間的變化而變化,采用粒子群優(yōu)化的灰色模型對動態(tài)關(guān)聯(lián)規(guī)則定義中的支持度向量和置信度向量做出趨勢預(yù)測,以便讓決策者及時掌握事情的發(fā)展動態(tài),為其做出決策提供參考依據(jù)。為了能夠更好的對關(guān)聯(lián)規(guī)則挖掘算法進(jìn)行研究,在閱讀了大量參考文獻(xiàn)之后,對國內(nèi)外現(xiàn)狀做出分析,發(fā)現(xiàn)了該領(lǐng)域當(dāng)前存在的一些問題,以此來提出本文所要研究的主要內(nèi)容。首先對關(guān)聯(lián)規(guī)則的基本概念及其原理、分類、經(jīng)典的算法和改進(jìn)的算法進(jìn)行介紹,關(guān)聯(lián)規(guī)則挖掘的目的和意義有了初步認(rèn)識,然后對動態(tài)關(guān)聯(lián)規(guī)則的定義和算法思想進(jìn)行分析,了解到動態(tài)關(guān)聯(lián)規(guī)則與關(guān)聯(lián)規(guī)則的區(qū)別,最后對粒子群優(yōu)化算法的原理、步驟以及對遺傳算法的比較做出分析,以便于為粒子群優(yōu)化算法和關(guān)聯(lián)規(guī)則算法相結(jié)合提供依據(jù)。針對經(jīng)典的Apriori算法在處理大型數(shù)據(jù)庫時挖掘效率有所下降,提出了一種基于二階粒子群的關(guān)聯(lián)規(guī)則挖掘算法。該算法共分四個步驟,首先第一步按照每個分區(qū)都能放進(jìn)內(nèi)存的原則,采用Partition算法對整個數(shù)據(jù)庫進(jìn)行不重疊劃分;其次采用Apriori算法對每個分區(qū)的數(shù)據(jù)集進(jìn)行關(guān)聯(lián)規(guī)則提取;然后采用二階粒子群優(yōu)化算法對挖掘得到的關(guān)聯(lián)規(guī)則進(jìn)行優(yōu)化分析,提取出一些易被忽略的有價值的規(guī)則;最后全局合并各個分區(qū)的關(guān)聯(lián)規(guī)則,并計算其實際的支持度和置信度。該算法不僅能夠減少數(shù)據(jù)庫的掃描次數(shù),而且能夠提取出因單個參考標(biāo)準(zhǔn)而被忽略的關(guān)聯(lián)規(guī)則。通過在Matlab平臺上實現(xiàn)該算法,在不同數(shù)據(jù)集上進(jìn)行了對比實驗,也對比了許多同類算法,實驗表明該算法是可行并且是有效的。針對動態(tài)關(guān)聯(lián)規(guī)則挖掘中規(guī)則變化趨勢的分析,提出一種改進(jìn)的粒子群優(yōu)化的灰色模型,該算法在粒子群算法中引入二次搜索機(jī)制,提高了算法的收斂性能,同時將其應(yīng)用到灰色模型中,優(yōu)化灰色模型在不同時刻的背景值,提高灰色模型的預(yù)測精度。通過在Matlab平臺上實現(xiàn)該算法,對比了不同算法的預(yù)測精度,實驗結(jié)果表明,預(yù)測精度達(dá)到了等級好的標(biāo)準(zhǔn),能夠滿足正常的預(yù)測需求。在對改進(jìn)的算法進(jìn)行了一系列的對比實驗,已經(jīng)能夠證明所要實現(xiàn)算法的可行性和有效性,但仍然需要在實際應(yīng)用方面做出實驗,本文選取了流動人口普查數(shù)據(jù)進(jìn)行關(guān)聯(lián)規(guī)則分析,首先選取跨省流動屬性作為依據(jù),分析跨省流動人員的特征,比如年齡、民族、戶口類型和受教育程度等,然后對跨省流動人員的流動原因進(jìn)行了關(guān)聯(lián)規(guī)則挖掘操作,得到流動原因的特征。通過兩方面的分析為相關(guān)部門加強(qiáng)人員管理方面提供建設(shè)性的意見,同時從挖掘結(jié)果來看證明了改進(jìn)算法的實際價值和意義,保證了算法研究的嚴(yán)謹(jǐn)性。
[Abstract]:Association rule analysis is the most important branch of data mining. Its main purpose is to excavate the hidden relationships or connections in the transaction database. With the popularization of large data, the problems of the traditional association rules mining algorithms are becoming more and more obvious, and the efficiency of the algorithm is also reduced. Particle swarm optimization algorithm (PSO) is used. As a representative of a population intelligent optimization algorithm, it has been widely used in different fields in recent years, including the analysis of association rules. By combining particle swarm optimization with association rules mining algorithm, this paper proposes an improved approach to association rule mining algorithm. In order to satisfy the rule mining of association rules mining, With the change of time, the grey model of particle swarm optimization is used to predict the trend of the support vector and confidence vector in the definition of dynamic association rules, so that the decision-makers can grasp the development trend in time and provide the reference for making decisions. In order to better the algorithm for mining association rules. After reading a large number of references, we have made an analysis of the current situation at home and abroad and found some existing problems in this field, so as to put forward the main contents of this paper. First, the basic concepts and principles of the association rules, the classification, the classical algorithms and the improved algorithms are introduced, and the association rules are excavated. The purpose and significance have a preliminary understanding, then the definition and algorithm of dynamic association rules are analyzed, and the difference between dynamic association rules and association rules is understood. Finally, the principle, steps and comparison of the genetic algorithms are made to the particle swarm optimization algorithm and the association rule algorithm. The efficiency of the classical Apriori algorithm in processing large databases has been reduced. A Association Rule Mining Algorithm Based on the two order particle swarm is proposed. The algorithm is divided into four steps. First, the first step is based on the principle that each partition can be put into memory, and the Partition algorithm is used for the whole database. Secondly, the Apriori algorithm is used to extract the association rules of each partition, and then the two order particle swarm optimization algorithm is used to optimize the mining association rules and extract some valuable rules that are easily ignored. Finally, the global merging of the association rules of each partition and the calculation of the actual support are also calculated. The algorithm can not only reduce the number of scanning of the database, but also can extract the association rules ignored by a single reference standard. Through the implementation of the algorithm on the Matlab platform, the comparison experiments on different data sets are carried out, and many similar algorithms are compared. The experiments show that the algorithm is feasible and has a good effect. According to the analysis of rule change trend in dynamic association rules mining, an improved grey model of particle swarm optimization is proposed. The algorithm introduces two search mechanism in particle swarm optimization, and improves the convergence performance of the algorithm. At the same time, the algorithm is applied to the grey model to optimize the background value of the grey model at different time and improve the grey model. The prediction accuracy of the color model is achieved by implementing this algorithm on the Matlab platform. The prediction accuracy of different algorithms is compared. The experimental results show that the prediction accuracy reaches a good grade standard and can meet the normal prediction requirements. A series of contrast tests on the improved algorithm have proved that the algorithm is feasible. And effectiveness, but still need to make experiments in practical application. This paper selects the data of the mobile population census to analyze the association rules. First, we select the cross provincial flow attribute as the basis to analyze the characteristics of the cross provincial mobile personnel, such as age, nationality, type of household registration and education, and then the reasons for the flow of migrants across provinces. The association rules mining operation is carried out, and the characteristics of the flow cause are obtained. Through the analysis of two aspects, it provides constructive suggestions for the relevant departments to strengthen the personnel management. At the same time, the actual value and significance of the improved algorithm are proved by the results of the mining, and the rigor of the algorithm is ensured.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13;TP18
本文編號：2159478
[Abstract]:Association rule analysis is the most important branch of data mining. Its main purpose is to excavate the hidden relationships or connections in the transaction database. With the popularization of large data, the problems of the traditional association rules mining algorithms are becoming more and more obvious, and the efficiency of the algorithm is also reduced. Particle swarm optimization algorithm (PSO) is used. As a representative of a population intelligent optimization algorithm, it has been widely used in different fields in recent years, including the analysis of association rules. By combining particle swarm optimization with association rules mining algorithm, this paper proposes an improved approach to association rule mining algorithm. In order to satisfy the rule mining of association rules mining, With the change of time, the grey model of particle swarm optimization is used to predict the trend of the support vector and confidence vector in the definition of dynamic association rules, so that the decision-makers can grasp the development trend in time and provide the reference for making decisions. In order to better the algorithm for mining association rules. After reading a large number of references, we have made an analysis of the current situation at home and abroad and found some existing problems in this field, so as to put forward the main contents of this paper. First, the basic concepts and principles of the association rules, the classification, the classical algorithms and the improved algorithms are introduced, and the association rules are excavated. The purpose and significance have a preliminary understanding, then the definition and algorithm of dynamic association rules are analyzed, and the difference between dynamic association rules and association rules is understood. Finally, the principle, steps and comparison of the genetic algorithms are made to the particle swarm optimization algorithm and the association rule algorithm. The efficiency of the classical Apriori algorithm in processing large databases has been reduced. A Association Rule Mining Algorithm Based on the two order particle swarm is proposed. The algorithm is divided into four steps. First, the first step is based on the principle that each partition can be put into memory, and the Partition algorithm is used for the whole database. Secondly, the Apriori algorithm is used to extract the association rules of each partition, and then the two order particle swarm optimization algorithm is used to optimize the mining association rules and extract some valuable rules that are easily ignored. Finally, the global merging of the association rules of each partition and the calculation of the actual support are also calculated. The algorithm can not only reduce the number of scanning of the database, but also can extract the association rules ignored by a single reference standard. Through the implementation of the algorithm on the Matlab platform, the comparison experiments on different data sets are carried out, and many similar algorithms are compared. The experiments show that the algorithm is feasible and has a good effect. According to the analysis of rule change trend in dynamic association rules mining, an improved grey model of particle swarm optimization is proposed. The algorithm introduces two search mechanism in particle swarm optimization, and improves the convergence performance of the algorithm. At the same time, the algorithm is applied to the grey model to optimize the background value of the grey model at different time and improve the grey model. The prediction accuracy of the color model is achieved by implementing this algorithm on the Matlab platform. The prediction accuracy of different algorithms is compared. The experimental results show that the prediction accuracy reaches a good grade standard and can meet the normal prediction requirements. A series of contrast tests on the improved algorithm have proved that the algorithm is feasible. And effectiveness, but still need to make experiments in practical application. This paper selects the data of the mobile population census to analyze the association rules. First, we select the cross provincial flow attribute as the basis to analyze the characteristics of the cross provincial mobile personnel, such as age, nationality, type of household registration and education, and then the reasons for the flow of migrants across provinces. The association rules mining operation is carried out, and the characteristics of the flow cause are obtained. Through the analysis of two aspects, it provides constructive suggestions for the relevant departments to strengthen the personnel management. At the same time, the actual value and significance of the improved algorithm are proved by the results of the mining, and the rigor of the algorithm is ensured.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13;TP18
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 于守健;周羿陽;;基于前綴項集的Apriori算法改進(jìn)[J];計算機(jī)應(yīng)用與軟件;2017年02期
2 郭世偉;孟昱煜;;一個基于二階粒子群的關(guān)聯(lián)規(guī)則挖掘算法[J];蘭州交通大學(xué)學(xué)報;2016年03期
3 張忠林;石皓尹;宋航;;灰色-周期外延模型的動態(tài)關(guān)聯(lián)規(guī)則元規(guī)則挖掘[J];計算機(jī)科學(xué);2014年04期
4 高海洋;沈強(qiáng);張軒溢;趙志軍;;一種基于數(shù)據(jù)壓縮的Apriori算法[J];計算機(jī)工程與應(yīng)用;2013年14期
5 張忠林;許凡;;基于小波變換的動態(tài)關(guān)聯(lián)規(guī)則元規(guī)則GM(1,1)挖掘[J];計算機(jī)科學(xué);2013年05期
6 孟昱煜;;基于云關(guān)聯(lián)規(guī)則的蟻群聚類算法研究[J];蘭州交通大學(xué)學(xué)報;2011年04期
7 李偉;袁亞南;牛東曉;;基于緩沖算子和時間響應(yīng)函數(shù)優(yōu)化灰色模型的中長期負(fù)荷預(yù)測[J];電力系統(tǒng)保護(hù)與控制;2011年10期
8 劉叢林;張忠林;曾慶飛;;PSO算法在關(guān)聯(lián)規(guī)則挖掘中的應(yīng)用[J];蘭州交通大學(xué)學(xué)報;2010年03期
9 張忠林;劉俊;謝彥峰;;AR-Markov模型在動態(tài)關(guān)聯(lián)規(guī)則挖掘中的應(yīng)用[J];計算機(jī)工程與應(yīng)用;2010年14期
10 沈斌;姚敏;;一種新的動態(tài)關(guān)聯(lián)規(guī)則及其挖掘算法[J];控制與決策;2009年09期
相關(guān)博士學(xué)位論文 前2條
1 何月順;關(guān)聯(lián)規(guī)則挖掘技術(shù)的研究及應(yīng)用[D];南京航空航天大學(xué);2010年
2 沈斌;關(guān)聯(lián)規(guī)則相關(guān)技術(shù)研究[D];浙江大學(xué);2007年
相關(guān)碩士學(xué)位論文 前5條
1 石皓尹;基于灰色系統(tǒng)理論的動態(tài)關(guān)聯(lián)規(guī)則挖掘研究[D];蘭州交通大學(xué);2014年
2 李顯龍;基于數(shù)據(jù)挖掘的內(nèi)蒙古流出人口特征分析[D];吉林大學(xué);2013年
3 金濤;PSO-SA算法的改進(jìn)及其在關(guān)聯(lián)規(guī)則挖掘中的應(yīng)用研究[D];華中師范大學(xué);2013年
4 段玉琴;數(shù)據(jù)挖掘中關(guān)聯(lián)規(guī)則算法的研究[D];西安電子科技大學(xué);2011年
5 劉進(jìn)鋒;動態(tài)關(guān)聯(lián)規(guī)則的理論與應(yīng)用研究[D];浙江大學(xué);2006年
，本文編號：2159478
本文鏈接：http://sikaile.net/kejilunwen/zidonghuakongzhilunwen/2159478.html
最近更新
教材專著
