粗糙集理論中數(shù)據(jù)約簡方法在電子商務(wù)中的應(yīng)用研究
發(fā)布時(shí)間：2018-09-04 19:41
【摘要】：粗糙集理論作為一種數(shù)學(xué)工具,能處理知識的模糊性和不確定性等問題。求核屬性和屬性約簡是粗糙集理論較為集中研究的課題,核屬性是所有屬性中最為核心的部分,在整個(gè)屬性約簡甚至最終的規(guī)則提取集中起到至關(guān)重要的作用；屬性約簡的目的是通過刪除不相關(guān)或不重要的屬性用盡量少而精的信息來表達(dá)原數(shù)據(jù)所表達(dá)的信息,已經(jīng)被證明是NP-hard問題。 本文在分析常用的求核屬性和屬性約簡算法的優(yōu)缺點(diǎn)時(shí)發(fā)現(xiàn),在眾多算法中大多只適用于相容決策表,而對決策表的不相容性考慮的甚少。本文提出了求核屬性和屬性約簡的分級差別矩陣算法,根據(jù)決策表是否相容而進(jìn)行不同的處理。在求核屬性中,因?yàn)樘幚聿幌嗳輿Q策表時(shí),現(xiàn)有文獻(xiàn)提出的改進(jìn)的差別矩陣求核方法比較合理和有效,所以保留其優(yōu)點(diǎn),在其思想的延伸下,提出分級差別矩陣方法,新方法是通過決策屬性的值進(jìn)行劃分,即論域的劃分,通過劃分的對象域形成分級差別矩陣,以分級差別矩陣和原有的差別矩陣得到的核可能是核屬性為前提,確定最終的核屬性。處理相容決策表時(shí),原有方法無法得到差別矩陣時(shí)可直接用本文的分級差別矩陣求核。兩差別矩陣求核方法有各自的優(yōu)缺點(diǎn),但是又有一定的聯(lián)系,實(shí)例證明本文提出的分級差別矩陣在原有差別矩陣得不到核的情況下,可以求出屬性核,證明了算法的有效性。把提出的分級差別矩陣運(yùn)用到屬性約簡方法研究中,以求核方法中得到的可能核為出發(fā)點(diǎn),求得約簡集,獲得決策表的約簡模型。實(shí)例分析驗(yàn)證了兩個(gè)算法的有效性。同時(shí)研究這兩個(gè)算法在電子商務(wù)數(shù)據(jù)約簡中的實(shí)際應(yīng)用。
[Abstract]:As a mathematical tool, rough set theory can deal with the fuzziness and uncertainty of knowledge. Finding kernel attribute and attribute reduction is a research topic of rough set theory. Kernel attribute is the core part of all attributes, which plays an important role in the whole attribute reduction and even the final rule extraction set. The purpose of attribute reduction is to express the information expressed by the original data by deleting irrelevant or unimportant attributes with as little and fine information as possible. It has been proved to be a NP-hard problem. After analyzing the advantages and disadvantages of the commonly used kernel attribute and attribute reduction algorithms, it is found that most of the algorithms are only applicable to the compatible decision table, but the incompatibility of the decision table is seldom considered. In this paper, a hierarchical discernibility matrix algorithm for kernel attribute and attribute reduction is proposed, which is treated differently according to the compatibility of decision table. In the kernel attribute, because the improved discernibility matrix kernel method proposed in the existing literature is more reasonable and effective when dealing with the incompatible decision table, the advantages of the improved discernibility matrix method are preserved, and the hierarchical difference matrix method is put forward under the extension of its thought. The new method is to divide the decision attribute by the value of decision attribute, that is, the division of the domain, and form the hierarchical discernibility matrix by dividing the object field. The kernel obtained by the hierarchical discernibility matrix and the original discriminant matrix may be the kernel attribute. Determine the final kernel attribute. When dealing with compatible decision table, the kernel can be directly obtained by using the hierarchical discriminant matrix when the original method can not get the discernibility matrix. The two difference matrix kernel method has its own advantages and disadvantages, but also has certain relations. The example proves that the hierarchical difference matrix proposed in this paper can work out the attribute kernel when the original difference matrix is not kernel, and proves the validity of the algorithm. The hierarchical difference matrix is applied to the study of attribute reduction. The reduction set is obtained and the reduction model of the decision table is obtained based on the possible kernels obtained from the kernel method. The effectiveness of the two algorithms is verified by an example. At the same time, the practical application of these two algorithms in e-commerce data reduction is studied.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F724.6;TP18
本文編號：2223146
[Abstract]:As a mathematical tool, rough set theory can deal with the fuzziness and uncertainty of knowledge. Finding kernel attribute and attribute reduction is a research topic of rough set theory. Kernel attribute is the core part of all attributes, which plays an important role in the whole attribute reduction and even the final rule extraction set. The purpose of attribute reduction is to express the information expressed by the original data by deleting irrelevant or unimportant attributes with as little and fine information as possible. It has been proved to be a NP-hard problem. After analyzing the advantages and disadvantages of the commonly used kernel attribute and attribute reduction algorithms, it is found that most of the algorithms are only applicable to the compatible decision table, but the incompatibility of the decision table is seldom considered. In this paper, a hierarchical discernibility matrix algorithm for kernel attribute and attribute reduction is proposed, which is treated differently according to the compatibility of decision table. In the kernel attribute, because the improved discernibility matrix kernel method proposed in the existing literature is more reasonable and effective when dealing with the incompatible decision table, the advantages of the improved discernibility matrix method are preserved, and the hierarchical difference matrix method is put forward under the extension of its thought. The new method is to divide the decision attribute by the value of decision attribute, that is, the division of the domain, and form the hierarchical discernibility matrix by dividing the object field. The kernel obtained by the hierarchical discernibility matrix and the original discriminant matrix may be the kernel attribute. Determine the final kernel attribute. When dealing with compatible decision table, the kernel can be directly obtained by using the hierarchical discriminant matrix when the original method can not get the discernibility matrix. The two difference matrix kernel method has its own advantages and disadvantages, but also has certain relations. The example proves that the hierarchical difference matrix proposed in this paper can work out the attribute kernel when the original difference matrix is not kernel, and proves the validity of the algorithm. The hierarchical difference matrix is applied to the study of attribute reduction. The reduction set is obtained and the reduction model of the decision table is obtained based on the possible kernels obtained from the kernel method. The effectiveness of the two algorithms is verified by an example. At the same time, the practical application of these two algorithms in e-commerce data reduction is studied.
【學(xué)位授予單位】：東北林業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F724.6;TP18
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