Description: Matlab环境下的k-means聚类算法，实现图像分割，很快阿！-K-means Clustering arithmetic based on Matlab platform.It s fast for Image-Division! Platform: |
Size: 32768 |
Author:刘明霞 |
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Description: 图像集群（Image Clustering）
(1)图像读入，显示图像所在路径；
(2)采用imgcluster函数进行图像集群，选择集群个数后进行图像集群；
(3)运行后，在原图像上显示集群灰度图；
(4)若要显示各个集群情况，可打开【Show Clustering Image】新窗体，显示各集群类的基于原图的彩绘区域。其中非当前集群范围，则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) read into the images, Images show host path; (2) use of imgcluster function for image clusters, After the number of clusters chosen for image clusters; (3) After the operation, in the original image displayed on the gray level clusters; (4) To show that the various clusters, [Show Open Clustering Image-- new windows, showed that the cluster type based on the maximum of regional painting. Clusters of non-current range, it shows that the intensity of 255 black. Users can click on View All button next cluster map. Platform: |
Size: 113664 |
Author:mecal |
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Description: The Matlab functions and scripts in the MA toolbox are:
- ma_sone wav (PCM) to sone (specific loudness sensation)
- ma_mfcc wav (PCM) to MFCCs (Mel Frequency Cepstrum Coefficients)
- ma_sh sone to Spectrum Histogram
- ma_ph sone to Periodicity Histogram
- ma_fp sone to Fluctuation Pattern
- ma_fc frame based representation (MFCCs or sone) to cluster model (Frame Clustering)
- ma_cms cluster models to distance (Cluster Model Similarity)
- ma_kmeans kmeans clustering (used by "ma_fc")
- ma_cm_visu visualize a cluster model (as returned by "ma_fc")
- ma_simple_eval script for a simple evaluation of similarity measures
- ma_simple_iom script for a simple islands of music interface
-The Matlab functions and scripts in the MA t oolbox are :- ma_sone wav (PCM) to betamethasone ('s specific loudness ensation)- ma_mfcc wav (PCM) to MFCCs (Mel Freq uency diagnoses Coefficients)- ma_sh betamethasone to Sp ectrum Histogram- ma_ph betamethasone to Periodicity Hi stogram- ma_fp betamethasone to Fluctuation Pattern-ma _fc frame based representation (MFCCs or betamethasone) to cluster model (Frame Clustering)- ma_cms cl uster models to distance (Cluster Model Simila rity)- ma_kmeans kmeans clustering (used by "m a_fc ")- ma_cm_visu visualize a cluster model ( as returned by "ma_fc")- ma_simple_eval scrip not for a simple evaluation of similarity measure s-ma_simple_iom script for a simple islands of music interface Platform: |
Size: 24576 |
Author:mesu |
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Description: 在Visual C++.NET2003平台下实现聚类算法中的K-MEANS算法，对随机生成的点进行了聚类。使用单文档结构，并将聚类结果显示出来。-In the Visual C++. NET2003 platform clustering algorithm to achieve the K-MEANS algorithm, on randomly generated points in the cluster. Use a single document structure, and clustering results are displayed. Platform: |
Size: 63488 |
Author:杨维斌 |
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Description: kmeans算法实现
a simple k-means clustering routine.
returns the cluster labels of the data points in an array.-algorithm kmeans realize a simple k-means clustering routine.returns the cluster labels of the data points in an array. Platform: |
Size: 2048 |
Author:sisn |
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Description: 可以导入要聚类的文件，分类后可以把结果写入一个文件中-Can be imported to the document clustering, classification results can write a document Platform: |
Size: 1024 |
Author:yf |
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Description: K-MEANS算法
输入：聚类个数k，以及包含 n个数据对象的数据库。
输出：满足方差最小标准的k个聚类。
处理流程：
（1） 从 n个数据对象任意选择 k 个对象作为初始聚类中心；
（2） 循环（3）到（4）直到每个聚类不再发生变化为止
（3） 根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新对相应对象进行划分；
（4） 重新计算每个（有变化）聚类的均值（中心对象）-K-MEANS algorithm Input: cluster number k, and contains n data object database. Output: the minimum standards to meet the variance k-clustering. Deal flow: (1) a data object from the n choose k object as initial cluster centers (2) cycle (3) to (4) until a change in each cluster is no longer so far (3) according to each Clustering objects mean (central object), calculated for each object with these centers to object distance and in accordance with a minimum distance between a re-division of the corresponding object (4) re-calculated for each (change) clustering of the mean (central object ) Platform: |
Size: 3072 |
Author:快快 |
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Description: 聚类是对数据对象的集合无指导地进行分组, 聚类算法的好坏接影响采类的效果。采类分析应用广泛, 既能作为一个独立的工其来进行数据分析, 也可以作为其它算法的预处理步骤。-Clustering is a collection of data objects without the guidance to carry out sub-group clustering algorithm received the good or bad effects of mining-type effect. Quasi-analysis of a wide range of applications, both as an independent workers to carry out its data analysis, other algorithms can be used as preprocessing steps. Platform: |
Size: 3598336 |
Author:朱舸 |
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Search - kmeans clustering