本文選題：聚類分析 + SOM算法　； 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：科技的飛速發(fā)展,引起信息的急劇膨脹,給計(jì)算機(jī)存儲(chǔ)和行業(yè)數(shù)據(jù)庫(kù)帶來巨大挑戰(zhàn)。隨著數(shù)據(jù)指數(shù)級(jí)的增大,維度不斷加大,數(shù)據(jù)類型的復(fù)雜度也在不斷提升。對(duì)于這些超高維數(shù)據(jù),需要通過數(shù)據(jù)挖掘技術(shù)來探索隱藏于數(shù)據(jù)內(nèi)的信息并利用獲取的信息輔助我們做出科學(xué)合理的預(yù)測(cè)與決策。常見處理高維數(shù)據(jù)方法有:數(shù)據(jù)降維、聚類分析、回歸分析等。本文介紹了傳統(tǒng)的自組織映射(SOM)神經(jīng)網(wǎng)絡(luò)和K-medoids算法。傳統(tǒng)的SOM算法在使用時(shí),存在部分樣本點(diǎn)和對(duì)應(yīng)的權(quán)向量之間差距較大,造成聚類的準(zhǔn)確性較低;K-medoids算法在聚類前需要人為確定聚類個(gè)數(shù)和初始中心點(diǎn),而不同的聚類個(gè)數(shù)和初始中心點(diǎn)的選擇會(huì)造成不同的聚類結(jié)果。為彌補(bǔ)以上兩種方法的不足,本文提出一種自組織映射(SOM)神經(jīng)網(wǎng)絡(luò)與K-medoids算法結(jié)合的算法——改進(jìn)的SOM-K算法。文中,第一章詳細(xì)描述了大數(shù)據(jù)背景下,聚類和降維算法的研究意義;第二章主要講述了基于聚類算法距離的定義;第三章主要闡述傳統(tǒng)的K-medoids算法和SOM算法;第四章主要說明了本文提出的基于SOM算法與K-medoids算法的改進(jìn)聚類算法并比較了傳統(tǒng)的K-medoids算法、SOM算法和SOM-K算法對(duì)鳶尾花數(shù)據(jù)集的聚類結(jié)果,證實(shí)了 SOM-K算法是優(yōu)于傳統(tǒng)的K-medoids算法和SOM算法的一種算法;第五章用SOM-K算法對(duì)于全國(guó)水資源分布進(jìn)行聚類分析并結(jié)合分析結(jié)果給出詳細(xì)的結(jié)論闡述;第六章進(jìn)行總結(jié)與展望,闡明改進(jìn)算法的優(yōu)勢(shì)與不足,以便后續(xù)繼續(xù)學(xué)習(xí)與探究。
[Abstract]:The rapid development of science and technology, causing the rapid expansion of information, computer storage and industry database brings great challenges. With the increase of data exponential level, the dimension is increasing, and the complexity of data type is also increasing. For these ultra-high dimensional data, we need to explore the information hidden in the data through data mining technology and use the obtained information to help us to make scientific and reasonable prediction and decision-making. Common methods to deal with high-dimensional data are: data dimension reduction, cluster analysis, regression analysis and so on. This paper introduces the traditional self-organizing mapping SOM) neural network and K-medoids algorithm. When the traditional SOM algorithm is used, there is a big gap between the partial sample points and the corresponding weight vectors, so the accuracy of the clustering algorithm is lower than that of the K-medoids algorithm. Before clustering, the number of clusters and the initial center points need to be determined artificially. Different clustering numbers and initial centers will result in different clustering results. In order to make up for the shortcomings of the above two methods, this paper presents an improved SOM-K algorithm, which combines the self-organizing mapping (SM) neural network with the K-medoids algorithm. In the first chapter, the research significance of clustering and dimensionality reduction algorithm under big data background is described in detail; the second chapter mainly describes the definition of distance based on clustering algorithm; the third chapter mainly describes the traditional K-medoids algorithm and SOM algorithm; In chapter 4, the improved clustering algorithm based on SOM algorithm and K-medoids algorithm is introduced, and the clustering results of traditional K-medoids algorithm and SOM-K algorithm for Iris data set are compared. It is proved that the SOM-K algorithm is superior to the traditional K-medoids algorithm and the SOM algorithm. Chapter 5 uses the SOM-K algorithm to cluster the distribution of water resources in China and gives a detailed conclusion. Clarify the advantages and disadvantages of the improved algorithm, so as to continue to learn and explore.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;TP183

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