【摘要】：區(qū)間概念格的參數(shù)優(yōu)化問題不同于傳統(tǒng)的參數(shù)優(yōu)化問題。來源于區(qū)間概念格的區(qū)間參數(shù),其取值直接影響著格結(jié)構(gòu)的規(guī)模和穩(wěn)定性,并對(duì)后續(xù)基于此挖掘出的關(guān)聯(lián)規(guī)則,分類規(guī)則的數(shù)目和精度以及決策準(zhǔn)則的效率產(chǎn)生著影響。目前人為主觀地選取區(qū)間參數(shù)值有很大的不確定性和弊端,鑒于此設(shè)計(jì)區(qū)間參數(shù)優(yōu)化模型。首先,數(shù)據(jù)量暴漲的現(xiàn)狀會(huì)導(dǎo)致格結(jié)構(gòu)中概念結(jié)點(diǎn)冗余,因此要進(jìn)行概念格的壓縮約簡(jiǎn)。結(jié)合形式背景下二元關(guān)系對(duì)與對(duì)象的近鄰的定義提出了區(qū)間概念格的壓縮算子,構(gòu)建了基于壓縮理論的區(qū)間參數(shù)優(yōu)化模型,通過調(diào)節(jié)壓縮度獲取格結(jié)點(diǎn)冗余最少時(shí)的區(qū)間參數(shù),并給出實(shí)例驗(yàn)證模型的有效性。其次,考慮到區(qū)間參數(shù)的改變只會(huì)改變部分概念結(jié)點(diǎn)和格結(jié)構(gòu),因此在之前的重建算法上提出了概念格的更新算法。結(jié)合基于區(qū)間概念格的關(guān)聯(lián)規(guī)則提取算法,提出了基于關(guān)聯(lián)規(guī)則的區(qū)間參數(shù)優(yōu)化模型。實(shí)例分析表明了當(dāng)區(qū)間參數(shù)取值接近[0.5,1]時(shí),由此挖掘出的關(guān)聯(lián)規(guī)則的數(shù)目適中且精度較高。再次,鑒于概念格本身對(duì)數(shù)據(jù)分類的特點(diǎn),設(shè)計(jì)了基于區(qū)間概念格的分類規(guī)則提取算法。發(fā)現(xiàn)當(dāng)區(qū)間參數(shù)改變時(shí),概念的分類規(guī)則的數(shù)目和精度都會(huì)隨之變化,由此提出基于分類規(guī)則的區(qū)間參數(shù)優(yōu)化模型,通過控制規(guī)則的數(shù)目和精度,達(dá)到調(diào)節(jié)區(qū)間參數(shù)的目的。實(shí)例驗(yàn)證了模型的有效性。最后,站在三支決策空間的角度上,給出了基于三支決策空間的區(qū)間參數(shù)優(yōu)化模型的應(yīng)用,通過圖書推薦案例討論了區(qū)間參數(shù)的改變對(duì)決策準(zhǔn)則的影響,進(jìn)一步驗(yàn)證了區(qū)間參數(shù)的有效取值,并達(dá)成近似一致。
[Abstract]:The parameter optimization problem of interval concept lattice is different from the traditional parameter optimization problem. The values of interval parameters derived from interval concept lattices directly affect the scale and stability of lattice structures and have an impact on the number and accuracy of classification rules and the efficiency of decision criteria. At present, there is great uncertainty and disadvantage in choosing the interval parameter value subjectively, in view of this design interval parameter optimization model. Firstly, the situation of data explosion will lead to the redundancy of concept nodes in lattice structure, so the reduction of concept lattice should be carried out. Combined with the definition of the nearest neighbor of the object under the formal background, the contraction operator of the interval concept lattice is proposed, and the interval parameter optimization model based on the compression theory is constructed. The interval parameters when the lattice node is least redundant are obtained by adjusting the compression degree. An example is given to verify the validity of the model. Secondly, considering that the change of interval parameters will only change some concept nodes and lattice structures, an updating algorithm for concept lattices is proposed in the previous reconstruction algorithms. Combined with the algorithm of extracting association rules based on interval concept lattice, an interval parameter optimization model based on association rules is proposed. The analysis of an example shows that the number of association rules is moderate and the precision is high when the interval parameter is close to [0.5 ~ 1]. Thirdly, in view of the feature of concept lattice to data classification, a classification rule extraction algorithm based on interval concept lattice is designed. It is found that the number and precision of the classification rules of the concept will change when the interval parameters change. Therefore, an optimal model of interval parameters based on the classification rules is proposed, which can adjust the interval parameters by controlling the number and precision of the rules. An example is given to verify the validity of the model. Finally, from the angle of three decision spaces, the application of interval parameter optimization model based on three-branch decision space is given, and the influence of the change of interval parameters on the decision criteria is discussed through book recommendation cases. The effective values of the interval parameters are further verified, and the approximate agreement is reached.
【學(xué)位授予單位】：華北理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O153.1

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