【摘要】：關(guān)聯(lián)規(guī)則挖掘是數(shù)據(jù)挖掘技術(shù)領(lǐng)域內(nèi)的研究重點(diǎn)和熱點(diǎn)之一,在各行業(yè)領(lǐng)域內(nèi)有著廣泛的應(yīng)用,Apriori算法作為關(guān)聯(lián)規(guī)則的代表性算法之一,其性能的好壞直接關(guān)系到關(guān)聯(lián)分析的效率和結(jié)論。目前,面對爆炸式增長的各類數(shù)據(jù),Apriori算法在處理時,其面臨問題也日益突出,主要體現(xiàn)在算法運(yùn)行時間長、效率低以及需要通過主觀單一設(shè)置最小支持度和最小置信度的閾值實(shí)現(xiàn)關(guān)聯(lián)規(guī)則提取這兩方面上。近年來,國內(nèi)外相關(guān)學(xué)者都對Apriori算法的改進(jìn)優(yōu)化進(jìn)行了研究,其中,將Apriori算法與其它智能算法融合進(jìn)行改進(jìn)是當(dāng)前一個研究熱點(diǎn),且在此研究方向上取得了豐碩的研究成果。結(jié)合以上情況,本文提出了一種DBS-PSO優(yōu)化算法對進(jìn)行優(yōu)化研究,其基本思路是:首先,利用改進(jìn)的密度偏差抽樣算法對原始數(shù)據(jù)集進(jìn)行抽樣,獲取樣本數(shù)據(jù);其次,通過設(shè)置適應(yīng)度函數(shù),利用改進(jìn)的粒子群算法迭代尋優(yōu)獲取解空間;最后,將粒子群算法求解的解空間作為Apriori算法中最小支持度和置信度的閾值,對樣本數(shù)據(jù)進(jìn)行關(guān)聯(lián)規(guī)則挖掘。實(shí)驗(yàn)仿真結(jié)果表明:在Apriori算法的優(yōu)化研究上,本文提出的DBS-PSO優(yōu)化算法,不僅降低了 Apriori算法的時間運(yùn)行成本,同時使得關(guān)聯(lián)規(guī)則的挖掘更為合理、客觀和高效。
[Abstract]:Association rule mining is one of the research focuses and hotspots in the field of data mining technology. It has been widely used in various fields. Apriori algorithm is one of the representative algorithms of association rules. Its performance is directly related to the efficiency and conclusion of correlation analysis. At present, in the face of explosive growth of all kinds of data, Apriori algorithm in processing, its problems are increasingly prominent, mainly reflected in the algorithm running time long, Low efficiency and the need to set a single subjective minimum support and the minimum confidence of the threshold to achieve association rules extraction. In recent years, scholars at home and abroad have studied the improved optimization of Apriori algorithm. Among them, improving the fusion of Apriori algorithm and other intelligent algorithms is a research hotspot, and has achieved a lot of research results in this research direction. Combined with the above situation, this paper proposes a DBS-PSO optimization algorithm for optimization research. The basic ideas are: firstly, the improved density deviation sampling algorithm is used to sample the original data set to obtain the sample data. By setting the fitness function, the improved particle swarm optimization algorithm is used to find the solution space. Finally, the solution space of the particle swarm optimization algorithm is used as the threshold of the minimum support and confidence in the Apriori algorithm. Mining association rules for sample data. The simulation results show that the DBS-PSO optimization algorithm proposed in this paper not only reduces the time cost of Apriori algorithm, but also makes the mining of association rules more reasonable, objective and efficient.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 韓家琪;毛克彪;夏浪;劉R，

本文編號：2267688