本文選題：聚類算法　切入點：大群體　出處：《湖南理工學院》2017年碩士論文

【摘要】：由于經(jīng)濟問題的復雜化,越來越多的決策成員參與其中,群體規(guī)模逐漸擴大,現(xiàn)有的群決策方法已經(jīng)不能滿足需求。決策問題的不確定性與復雜化,也決定了決策者的評價信息也必須從多個屬性出發(fā),在此同時,由于人類思維的模糊性和不確定性,決策者的評價信息多數(shù)為模糊數(shù),區(qū)間數(shù)尤為簡單且常見,但目前對區(qū)間型多屬性大群體決策研究還較少。本文針對屬性權重信息完全已知和屬性值為區(qū)間型隨機變量的多屬性大群體決策問題,提出了一種大群體決策方法。本文主要研究工作如下:首先,針對傳統(tǒng)K-均值聚類算法對初始聚類中心的依賴性,提出了優(yōu)選初始聚類中心的改進K-均值聚類算法。在考慮數(shù)據(jù)集實際分布的基礎上,選擇與實際聚類中心接近的樣本點作為初始聚類中心,結果顯示,在經(jīng)過較少的迭代就可以得到穩(wěn)定、準確率較高的聚類結果。然后,本文提出了區(qū)間型數(shù)據(jù)相似性度量標準,通過數(shù)學證明能滿足相似度的基本性質,適合任意分布的區(qū)間數(shù)之間的相似性度量。在此基礎上,本文結合改進K-均值聚類算法對區(qū)間型多屬性大群體決策問題聚類分析,得到較好的聚類結果,證明了本文相似度定義式是有效的。其次,本文在對大群體數(shù)據(jù)聚類過程中,對比不同類別數(shù)情況下得到的聚類效果,由于類內(nèi)相似度越大,同時類間相似度越小,聚類效果越好。因此計算類內(nèi)與類間相似度比值,選擇比值最大的類別數(shù)作為最佳聚類類別數(shù)。最后,本文對類權重的設定時,考慮了區(qū)間數(shù)據(jù)存在不確定性,以及群決策中以最大滿意度為目標,提出與類權重密切相關的三個因素:區(qū)間寬度、類內(nèi)評價信息的緊致性、類成員比重。相比于其他權重賦值方法,本文賦權方法更科學,更全面。
[Abstract]:Due to the complication of economic problems, more and more decision making members participate in it, and the group size is gradually expanded. The existing group decision making methods can not meet the demand.The uncertainty and complexity of the decision making problem also decide that the evaluation information of the decision maker must also proceed from many attributes. At the same time, due to the fuzziness and uncertainty of human thinking, most of the evaluation information of the decision maker are fuzzy numbers.Interval number is especially simple and common, but there are few researches on interval type multi-attribute large group decision making.In this paper, a large group decision making method is proposed to solve the problem of large group decision making in which the attribute weight information is completely known and the attribute value is interval random variable.The main work of this paper is as follows: firstly, an improved K-means clustering algorithm is proposed to optimize the selection of initial clustering centers in view of the dependence of the traditional K-means clustering algorithm on the initial clustering centers.On the basis of considering the actual distribution of the data sets, the sample points close to the actual clustering centers are selected as the initial clustering centers. The results show that the clustering results are stable and accurate after fewer iterations.Then, this paper presents the similarity measure standard of interval data. It is proved by mathematics that it can satisfy the basic properties of similarity degree and is suitable for the similarity measure between interval numbers with arbitrary distribution.On this basis, this paper combines the improved K-means clustering algorithm to cluster analysis of multi-attribute large group decision making problem with interval type, and obtains a better clustering result, which proves that the similarity definition in this paper is effective.Secondly, in the process of large group data clustering, compared with the different categories of the results of clustering, because the greater the intra-class similarity, at the same time, the smaller the similarity between the clusters, the better the clustering effect.Therefore, the ratio of intra-class similarity and inter-cluster similarity is calculated, and the number of categories with the largest ratio is selected as the best cluster number.Finally, considering the uncertainty of interval data and the goal of maximum satisfaction in group decision making, this paper proposes three factors closely related to class weight: interval width, compactness of in-class evaluation information.Class member specific gravity.Compared with other weight assignment methods, this method is more scientific and comprehensive.
【學位授予單位】：湖南理工學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;O225

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本文編號：1702782