Description: 首先介绍模糊集基本知识
其次K均值聚类算法(HCM)介绍
最后重点介绍了模糊C均值聚类，
模糊聚类是一种很重要思想，最近再图像处理中就用的了这种思想，也算是一点思维创新-First of all, introduce basic knowledge of fuzzy sets was followed by K-means clustering algorithm (HCM) focuses on the introduction Finally fuzzy C-means clustering, fuzzy clustering is a very important thinking, recently re-image processing on the use of such thinking, but also Innovative thinking is that Platform: |
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Author:huiguiyang |
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Description: ClustanGraphics聚类分析工具。提供了11种聚类算法。
Single Linkage (or Minimum Method, Nearest Neighbor)
Complete Linkage (or Maximum Method, Furthest Neighbor)
Average Linkage (UPGMA)
Weighted Average Linkage (WPGMA)
Mean Proximity
Centroid (UPGMC)
Median (WPGMC)
Increase in Sum of Squares (Ward s Method)
Sum of Squares
Flexible (ß space distortion parameter)
Density (or k-linkage, density-seeking mode analysis) -ClustanGraphics clustering analysis tools. Provides 11 kinds of clustering algorithms. Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean ProximityCentroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of SquaresFlexible (? space distortion parameter) Density (or k-linkage, density-seeking mode analysis) Platform: |
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Author:wangyexin |
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Description: 文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层
到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索
路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法
无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum. Platform: |
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Author:luhui |
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Description: 改进的k-means方法，对聚类的实例节能型加权 少数类多数类的函数-Improved k-means method for clustering a small number of examples of energy-saving type of weighted majority of types of function Platform: |
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Author:zhangwen0927 |
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Description: 用Ｋ均值和遗传算法实现了半监督聚类算法，这是个一个已经发表的论文的源程序-Using K-means and genetic algorithm to achieve a semi-supervised clustering algorithm, this is a paper published source Platform: |
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Author:张帅 |
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Description: 程序很好的实现了k均值的聚类，我已经使使用过，效果非常好，很好用-Program achieved a very good k mean clustering, I have so used, the effect is very good, very good use Platform: |
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Author:赵红丽 |
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Description: the classic pattern recognition algorithms, dynamic clustering algorithm k mean using Matlab programming, as well as classification of the class analysis Platform: |
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Author:jntu |
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Description: K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm Platform: |
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Author:renli |
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Description: Intention based scoring (IBS) is a scoring system, its goal is to provide a score that more accurately assesses a student’s ability to solve a composition problem, and therefore assess direct effects of the student s programming ability. This process is dubbed “intention based” because, firstly, the programs are inspected by inferring what goals the student is trying to achieve, and secondly, what plan component is being attempted when a program statement is not syntactically correct.
The three steps necessary to perform IBS were introduced by [1]: inspection of an on-line protocol, bug identification, and scoring.
The final goal of this project is to create an automated Intention Based Score for each programming assignment, and use K-Mean Clustering to identify which students belong to which group. Platform: |
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Author:ahmad |
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Description: k均值聚类算法 ，c语言实现 了基于均值的聚类分析，同时增加了多维向量分析功能，使得聚类的收敛速度更快。-k means clustering algorithm, c language implemented based on the mean cluster analysis, while increasing the multi-dimensional vector analysis functions, making the convergence faster clustering. Platform: |
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Author:pulutarch |
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Description: 根据聚类结果，对已经聚类好的数据进行处理，并利用svg画出各部分的趋势图。-According to the clustering results, clustering of good data has been processed, and used to draw the various parts of the svg trends. Platform: |
Size: 2048 |
Author:huangfei |
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Description: Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application
domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of Oriented Gradients, i.e. HOG, features. A K-mean clustering technique is used in a cascade of HOG feature classifiers to detect faces. The evaluation of the algorithm shows similar performance in terms of detection rate as state of the art algorithms. Moreover, unlike state of the art algorithms,our system can be quickly trained before detection is possible. Performance is considerably increased in terms of lower computational cost and lower false detection rate when combined with motion constraint given by moving objects in video sequences. The detected HOG features are integrated within a tracking framework and allow reliable face tracking results in several tested surveillance video sequences. Platform: |
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Author:linuszhao |
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