clustering algorithm 中文意思是什麼
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4. an object detection method with em ( expectation maximum ) algorithm of dynamic layer representations is researched and improved. previous algorithm contains optical flow computation, affined transformation, and clustering algorithm, and it is not convenient for detecting object quickly
4 .分析並改進了基於em （ expectationmaximum ）演算法的運動目標分層檢測演算法，早期演算法由於涉及光流場求解、仿射變換、聚類合併等復雜運算，計算量大，不適合圖像序列的快速處理。 -
In accordance with the problem that the fcm algorithm is quite time - consuming for search out cluster cancroids and may not be suitable for on - line modeling and control. this dissertation proposed an improved fuzzy identification method based multistage random sampling fuzzy c - means clustering algorithm ( mrfcm ). it has higher approximate precision and the cpu time has slowed down sharply compared with the common fuzzy
Johnyen和liangwang介紹了幾種應用於模糊模型的信息優化準則，本論文在此基礎上對統計信息準則進行一些改進，並與快速模糊聚類和正交最小二乘方法結合，提高了模型的辨識精度和泛化能力。 -
Based on the careful analysis of present clustering algorithm, we give two text clustering algorithms : ek ( exact k - means algorithm ) and dbtc ( density - based text clustering ), and discuss the results of clustering experiments
對現有聚類演算法進行了仔細分析，給出了兩個文本聚類演算法： ek演算法和dbtc演算法。對這兩種演算法進行了詳細介紹，並分析了聚類實驗的結果。 -
The experimental results show that the method can not only detect the fuzzy edge and exiguous edge correctly, but also improve the searching efficiency of fuzzy clustering algorithm based on tea evidently
實驗結果表明，該演算法不僅具有很強的模糊邊緣和微細邊緣檢測能力，而且可以提高基於人工免疫進化演算法的模糊聚類演算法的搜索效率。 -
Dilc : a clustering algorithm based on density - isoline
等密度線演算法 -
Immune clone strategy is introduced into c - means algorithm, which can effectively tackle those problems of nonstability, slow convergence and nonideal clustering that exist in ids with the traditional c - means. the experimental results reveal that the system can detect variety of unknown abnormal intrusions, and demonstrate that our combined clustering algorithm has good performance
實驗結果證明該上述兩種演算法有效地克服了傳統c -均值聚類演算法在解決入侵檢測問題中的穩定性差、收斂性不好和聚類效果不理想等問題，並能在一定程度上檢測到未知的異常入侵行為。 -
Subsequently, clustering analysis in data mining is disserted, involving the methods and characteristics of clustering used in data mining and the methods for evaluating the clustering results, with emphasis on clustering the data with categorical attributes. k - modes clustering algorithm and its variations are introduced with their advantages and disadvantages
在此基礎上對數挖掘中的聚類分析作以詳細地論述，總結了數挖掘中聚類分析的方法和特點，並對聚類結果的評價方法進行了討論，重點討論了分類屬性數據聚類，具體研究了k - modes演算法及其變形，並指出了它們的優缺點。 -
The innovations of this thesis can be summarized into three points. firstly, the average relative velocity is introducd into a novel adptive weighted clustering algorithm as one important parameter of weight, then it increases the stability and self - adaptability of cluster head. secondly, a new approach to calculating weight is suggested by integrating subjective and objective factors. it is verified by comparison with other approaches to selecting weight. thus the velocity of weight responding to the changes of network topology is increased. finally, using a som neural network to create a classifying model enables every node to learn to identify by itself the role in manet
本文的創新點有三個:首先本文在wca和aow分簇演算法的基礎上，引入了平均相對移動速度作為權值重要的參數，提出了一種新的基於權值的自適應分簇演算法，提高了簇頭在移動中的穩定性和自適應性;其次，提出了利用主客觀綜合賦權法確定權重的權值計算方法，通過與其他權重選擇方法比較，網路結構變化的權值響應速度得到了改進;最後，論文利用自組織特徵映射神經網路建立分類模型，使得網路中的節點可以自學習地確定簇中角色。 -
This thesis includes four parts in which the technologies of web usage mininig are systematically researched. in the first part we summarize the techniques of data mining and web usage mining, present the significance of the research on web usage mininig, the status of research and the problem which web usage mininig will face with. in the second part we discuss the web usage mininig according to the process of web mining. in the stage of data preparing and preprocessing we discuss the algorithm of data cleaning, user and session identification in detail, and present a data model of association rules and sequential patterns in the stage of pattern discovery, discuss the useful method of pattern analysis in last stage. a synthesis clustering algorithm cppc is proposed in the third part of this thesis
本文分主要從以下四個方面對web使用挖掘進行了系統的分析和研究。第一是對數據挖掘和web挖掘進行了概述，闡述了web挖掘的意義、研究的現狀、面臨的問題。第二是討論了web使用挖掘的三個階段：在數據準備和預處理階段重點討論了數據清洗及用戶和會話識別演算法；在模式發現階段定義了關聯規則和序列模式的數據模型；模式分析階段則討論了現行的幾種分析方法。 -
Second, we design a chinese text clustering model ctcm and research main aspects of ctcm such as feature presentation, feature extraction, the adjust of feature vector and clustering algorithm. third, we lay emphasis on the study of text clustering algorithm
然後，我們設計了一個中文文本聚類模型ctcm （ chinesetextclusteringmodel ） ，並針對模型中涉及到的特徵表示、特徵提取、特徵向量調整和聚類演算法等問題進行了研究。 -
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 -
The euclidean distance is usually chosen as the similarity measure in the conventional k - means clustering algorithm, which usually relates to all attributes
傳統的k -均值演算法選擇的相似性度量通常是歐幾里德距離的倒數，這種距離通常涉及所有的特徵。 -
Crowding model is used to form multiple niches in fitness landscape, while clustering algorithm eliminates genetic drift in each inner niche
擁擠模型在適應值曲面上形成多個小生境，聚類演算法消除了每個小生境內部的基因漂移現象。 -
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。 -
On the improvement of k - means clustering algorithm
均值聚類演算法的研究 -
The performance of k - means clustering algorithm depends on the selection of distance metrics
K -均值（ k - means ）演算法聚類的結果依賴于距離度量的選取。 -
3 ) k - mean clustering algorithm is used classification. the category is two, according to the object and the experience knowledge
3 ）應用無監督分類演算法中k均值（ k - means ）聚類演算法對輸入特徵向量進行分類。 -
This paper studies using the k - means clustering algorithm to classified the obtained image, submits through two phases to retrieve the image and adjusts the weight using the relevant feedback method
本文研究利用k均值聚類方法對檢索得到的圖像進行分類，通過兩階段提交對圖像進行篩選，並利用相關反饋方法來調整權重。 -
Fast clustering algorithm based on probability and morphology
基於概率分佈和形態學的快速聚類演算法 -
The colors of woven fabric patterns are identified automatically by using the combined technique of a fuzzy c - means clustering algorithm and template matching, and the colour images of color - yarn arrangements and the woven fabric images classificated in colors are obtained, which can be used for fabric pattern recognition and template matching identifition
摘要採用模糊聚類與模板疊合判定技術相結合的方法實現了色織物組織點顏色的自動判析，得到色紗的排列圖和顏色分類后的織物圖像，可用於進一步的組織識別和疊合驗證。
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