聚類中心法 的英文怎麼說
中文拼音 [jùlèizhōngxīnfǎ]
聚類中心法
英文
method of clustering center-
The thesis discuss the data mining principle and technology from application and the fact of guizhou telecom. by uniting the designing and developing processes, the system cleanout, change, loading the volume custom operation data into the data warehouse that used many analyst methods such as clustering analyst, forecast analyst to make the function of subdividing custom colony and running and improved the competition core ability of enterprise
同時，針對貴州電信海量的客戶運營數據，經過清洗、轉換、整合到數據倉庫中，採用了多種分析方法，如聚類分析、預測分析，實現了客戶群細分、客戶流失預測等功能，極大的提高了貴州電信核心競爭力。The three kinds of simulated point targets are designed and the rangeprofiles at aspect angle are computed. the radar target recognition method based on the optimal cluster centers is simulated and studied. it is discovered that the algorithm is effective when there are lots of training data, but noneffective when there are only a few training data
2 、對最優聚類中心目標識別法進行模擬實驗並研究其識別性能，實驗結果表明該演算法在大樣本訓練數據時能得到較高識別率，是一種有效、可行的識別演算法，但在少樣本訓練數據時，所得識別率急劇下降。A novel dynamic evolutionary clustering algorithm ( deca ) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. deca searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes ; at the same time, the convergence of clustering center parameters is expedited with the help of fuzzy c - means ( fcm ) algorithm. moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, deca can converge to the optimal solution rapidly and stably. the proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient deca to identify fuzzy models. the effectiveness of the proposed fuzzy modeling method based on deca is demonstrated by simulation examples, and the accurate non - linear fuzzy models can be obtained when the method is applied to the thermal processes
針對模糊聚類演算法不適應復雜環境的問題，提出了一種新的動態進化聚類演算法，克服了傳統模糊聚類建模演算法須事先確定規則數的缺陷.通過改進的遺傳策略來優化染色體長度，實現對聚類個數進行全局尋優;利用fcm演算法加快聚類中心參數的收斂;並引入免疫系統的記憶功能和疫苗接種機理，使演算法能快速穩定地收斂到最優解.利用這種高效的動態聚類演算法辨識模糊模型，可同時得到合適的模糊規則數和準確的前提參數，將其應用於控制過程可獲得高精度的非線性模糊模型In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local
本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。In connection with the difference and distribution characteristic of the samples in sample space rs based on dga, a new self - adapted weight fuzzy omean clustering model of fault diagnosis of the power transformer based on the potential function is proposed. meanwhile, from the aspect of geometry characteristic of fc - divided in s dimension sample space, a method is proposed for the purpose of getting an effective adjacent radius, adaptive cluster number c and original cluster center of x sample set. for the diagnosis sample x, the property measure and diagnosis rule are proposed, which under the condition of potential density function that determine c number of optimal fuzzy cluster p1
根據以變壓器dga數據為特徵量的樣本空間各樣本差異特性以及樣本在空間r ~ s的分佈特性，首次提出了基於勢函數自適應加權的變壓器絕緣故障診斷的模糊c -均值聚類模型；同時，從s維樣本空間的f ~ c -劃分幾何特性出發，提出了一種求取樣本集的類勢有效鄰域半徑和自適應求取聚類數和聚類中心初值的方法；對一個待診斷樣本，設計了基於類勢密度函數意義下的屬性測度和診斷準則。Be aimed at the characters of fluvial layered pool, such as serious intrastratal and interlayer heterogeneity, small water flooding volume and low oil displacement efficiency in its high water - cut stage, the fine research work on reservoir heterogeneity model are carried out, and the research is done on the base of geological, logging, production testing materials and production date and with a center of the research of remaining oil. the west 7th block, gudong oilfield is in case. the forming mechanisms of remaining oil and its distributing feature in this area are revealed
本文針對我國陸相沉積層狀油藏層內及層間儲層非均質性嚴重、高含水期水驅波及體積小、驅油效率低等特點，以剩餘油研究為中心，藉助于數學地質統計及聚類分析等方法，綜合利用孤東七區西的地質、測井、生產測試資料和生產動態信息等，深入開展了儲層非均質模型的精細研究，揭示了在不同規模非均質模型上剩餘油的形成機理和分佈特徵。The paper puts forward the clustering algorithm includes : clustering based on grid and iterative, enhanced clustering algorithm base on density and k - medoids, enhanced k - means algorithm ( optimize chooseing consult _ points in iterative process ), enhanced clustering algorithm base on distance. they can overcome many limitations ( some traditional algorithms terminate in local optimization. many results of cluster are roundness, too many times in partition iterative process ), which are related to the static architecture of traditional model
在傳統聚類演算法的基礎上，結合我們科學數據挖掘的應用對象?分子動力學數據，提出了迭代網格聚類演算法， k -平均和基於密度結合的聚類演算法，迭代過程中優化選擇中心點的k -平均方法，以及改進型的基於距離的聚類演算法等模式識別方法，能夠解決傳統演算法帶來的諸多問題（比如一些傳統的聚類演算法常常收斂于局部最優，發現都模式都趨近於球形，劃分方法中迭代次數過多帶來的效率問題） 。Better segmentation effect can be attained by coding gray levels of pixels as eigenvector, taking advantage of histogram entropy principles function as fitness function, adopting ranking selection operation, making use of arithmetic crossover and mutation at a certain probability, combining with clustering analysis to initialize clustering center of the population to segment cells image with genetic algorithm
以像素的灰度值為特徵向量進行編碼，利用直方圖熵法準則函數作為適應度函數，採用基於排名的選擇操作，以一定的概率進行算術交叉和變異，並結合聚類分析設定種群的聚類中心對細胞圖像進行遺傳聚類分割。Cf is a kind of non - different recommendation to all customers, so it is may appear that the company spend much cost but have little revenue. recently a new recommend technology base on lifetime value of customers have emerged, the centre of the technology is to use the weighed sum of the three variable to classify the customers and the reason of use weighed variable is that think about the relative important degree. then we can recommend products according to the association rule in the classify of same clv. the shortcoming of this recommendation is that the recommendation may have some error when the customers ’ data is not enough to form regular value
近年來出現了一種採用基於客戶生命周期值的加權rfm （表示客戶生命周期的三個指標變量）技術的推薦方法，這一技術的核心思想就是採用這三個客戶屬性變量的加權和的方法來對客戶進行聚類，其中為變量加權是考慮到不同變量對顧客產生的不同重要程度的影響。對符合某一生命周期類型的客戶按照商品挖掘關聯技術進行商品推薦。這種推薦方法的弊端在於當客戶的交易數據不足以形成規律化的量值時，推薦可能會出現偏差。With the swot analysis method, the article deeply analyzed the foundation of strategy capability of the company by associating with the analyses of outer environments and inner conditions to provide the ideas and evidences for the establishment of strategy planning program on the basis of analyses in both theory and practice, in accordance with the established strategy factors and selected general strategy methods, the article made out the strategy planning program and strategy target of the company and pointed out various subsystems of strategy planning programs such as the products strategy plan, market strategy plan, finance strategy plan, manpower resource plan, organization & administration development plan, technology development plan and technology transformation plan etc finally the article applied life cycle method and economic evaluation method to evaluate and analyze the strategy planning program stated in this article so as to guarantee that the research conclusions are scientific and practicable
再次，對株航公司的戰略資源和能力進行了系統的分析和閘述，綜合了中、小發基地建設已有的基礎，結合行業現狀明確了株航公司的核心能力，並運用swot分析方法，綜合企業外部環境分析和內部條件分析，深入分析了株航公司的戰略能力基礎，為制定戰略規劃方案提供了思路和依據。在對理論和實際進行研究分析的基礎上，運用戰略聚類模型分析方法，按照戰略制定要素並結合總體戰略選擇方法，擬定了株航公司的戰略規劃方案和戰略目標，並提出了產品開發戰略規劃、市場戰略規劃、財務戰略規劃、人類資源規劃、組織與管理發展規劃、技術發展規劃、技術改造發展規劃、等各子系統的戰略規劃方案。最後，綜合運用生命周期法和經濟效果評價等研究方法對本戰略規劃進行了評估分析，保證了研究結論的科學性和實用性。It ’ s core spirit is transforming badly - conditioned matrixes to well - conditioned matrixes, then accurate recognition results are obtained. it inherits advantage of the recognition method based on the optimal cluster centers
該演算法在少樣本訓練數據時能得到較高識別率，同時具有最優聚類中心法的優點，在大樣本訓練數據時仍有較高識別率。In allusion to the present situation of spatial structure of dunhuang circle and the fact of tourism development, the study is on spatial structure optimization and puts forward optimizing mode on the developing tenet of sustainability the thesis includes six chapters, among which the third chapter, fourth chapter and fifth chapter are the major body. in the foreword, it expounds the background, foundation, purpose, meaning and adoptive technical courses of the selecting thesis ' s topic. in the first chapter, it summarizes main theories of tourism spatial structure and the present researches of tourism circle from study of theory and demonstration
引言部分主要闡述了論文選題的背景、依據、目的和意義以及擬採用的技術路線；第一章總結了旅遊空間結構當前的主要理論，綜合論述了當前旅遊空間結構的研究進展，對旅遊圈的研究現狀從理論研究和實證研究兩個方面進行了概述；第二章介紹了敦煌旅遊圈的總體概況，分析了構建敦煌旅遊圈的動力因素和制約因素；第三章採取層次分析法和模糊聚類法對敦煌旅遊圈內旅遊資源進行了分析評價；第四章採用客源平均中心點、客源吸引半徑、地理集中度指數等方法對敦煌旅遊圈客源市場進行了分析評價；第五章在前幾章分析評價的基礎上，分析了敦煌旅遊圈的空間結構，提出了敦煌旅遊圈的空間優化模式，並提出了敦煌旅遊圈的發展原則及具體措施。At the realization of the system, we analyze the integral structure and working principle of our system at first. then, we show the relationship among tables in core database. lastly, we study automatic document categorization algorithm and propose algorithm descriptions and experiment results of chinese language segmentation, schema matching of paper titles and clustering
在系統實現部分分析了系統的整體結構和工作原理，介紹了系統核心數據庫中各表的聯系，最後重點研究了文檔自動分類演算法，給出了漢語分詞演算法、論文標題模式匹配演算法、聚類演算法的演算法描述及實驗結果。In the new algorithm, the applications of improved subtractive clustering for initialization, part feature based in distance function and the best solution selection procedure eliminate the clustering errors of fcm, and the clustering efficacy is improved dramatically
該演算法中提出的改進的減法聚類法產生初始聚類中心、基於零件特徵的距離函數、迭代方案的選擇方法等基本消除了fcm的聚類錯誤，顯著的提高了成組效率。The reason of the above error is found out : the recognition method based on the optimal cluster centers disposes badly - conditioned matrixes inaccurately. so, an improved approach based on optimal cluster centers is proposed
針對這個缺點，提出改進最優聚類中心目標識別法，核心思想是把「病態」矩陣進行「良態」化處理后再參加運算，以得到正確識別結果。Firstly, the initial values of cluster are obtained by hough transform, which consider the linearity and continuity, then the premise and consequent parameters are identified based on fuzzy c - means and recursive least square
首先利用hough變換的方法得到聚類中心的初始值，然後通過模糊c -均值聚類法辨識前提參數，採用遞推最小二乘辨識模糊模型的結論參數。( 3 ) taking into account the fuzzy characteristics existed in selecting of the representative year, the fuzzy clustering method is adopted to the cluster analysis of year runoff data, and the cluster center vectors standing for the type of year, that is, abundant, moderate and deficient, are obtained
（ 3 ）考慮到典型年判定時存在的模糊性，將模糊聚類法用於年徑流分析，由此求得分別代表豐、平、枯各種年型的聚類中心向量。The main contributions of this paper include the following : ( 1 ) an effect method for computing inter - document similarities based on content and link analysis ; ( 2 ) identifying the advantages of fuzzy clustering methods in comparison with normal clustering methods, and presenting a fuzzy ( soft ) clustering algorithm htsc base on a mixed similarity metric of content and link ; ( 3 ) theoretic analysis and preliminary experiments of the algorithm
論文的主要貢獻是： （ 1 ）給出了基於文檔文本內容和文檔間超鏈信息的混合相似度計算方法。 （ 2 ）比較了一般的聚類方法及模糊聚類方法的優缺點，並給出了基於混合相似度的模糊（軟）聚類演算法htsc 。 （ 3 ）對htsc演算法進行了理論分析，並對其中的核心演算法進行了初步的實驗驗證。The main results of our paper are as follows : present a chinese text clustering model ; obtain a chinese text algorithm which can select better initial point and a dbtc clustering algorithm which can identify cluster with any shape
本文獲得的主要結果：提出了一個中文文本聚類模型，提出一種選取初始聚類中心的中文文本聚類演算法和一種能發現任意形狀簇的dbtc聚類方法。This network can be summarized as follows : ( 1 ) about the networks " architecture, it is not fully connected but it uses selective connection between the units of two hidden layers. the number of these units is determined dynamically. ( 2 ) during the learning procedure, a new input - output clustering ( ioc ) method is adopted to select centers
該網路主要有以下特點： （ 1 ）網路結構上，兩層隱層選擇性連接，隱層節點數在學習過程中動態確定； （ 2 ）學習規則上，提出一種同時考慮輸入輸出樣本信息的輸入一輸出聚類（ input - outputclustering ， ioc ）方法，且聚類中心的形狀參數自適應變化。分享友人