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: 24961 |
Author:mesu |
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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. Platform: |
Size: 2087 |
Author:sisn |
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Description: 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or) Platform: |
Size: 1024 |
Author:阿兜 |
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Description: k-means聚类算法源码。kmeans是一种常用的分割算法，简单而又高效。-k-means clustering algorithm source code. Kmeans is a common segmentation algorithm is simple but efficient. Platform: |
Size: 1024 |
Author:熊健 |
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Description: k均值聚类算法分析，采用C++编程技术，提供代码可重用机制 -k-means clustering algorithm analysis, the use of C programming, code reuse mechanism Platform: |
Size: 30720 |
Author:chu na |
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Description: 程序说明：
Form1.cs是应用聚类算法DBSCAN (Density-Based Spatical Clustering of Application with Noise)的示例，可以通过两个参数EPS和MinPts调节聚类。
DBSCAN.cs是实现文件，聚类算法的进一步信息请参考“数据挖掘”或者相关书籍
聚类示例数据来自于sxdb.mdb，一个Access数据库。
已知问题及进一步改进建议：
问题：dbscan.cs行64，SortedList不支持重复键，因此若两个数据点距离相同则无法加入集合
解决：采用人为减小一个微小量，使数据点距离不同且不影响聚类结果
上一解决方案的问题：减小double.Epsilon微小量无助于使SortedList认为两点距离以及不同
解决：采用一个指数增长的微小量，连续重试直至SortedList认为距离已经不同
进一步改进建议：可能通过double的强制转型为内存中的byte类型（假设double型转为8个byte）
然后最后一个byte减去0x01可比较漂亮的解决问题，但是……呵呵，C#中我不会这个操作
也可以自己实现一个SortedList，支持重复键，当然，这，好像是微软应该做的工作了 ^_^
Eric Guo
<http://www.cnblogs.com/ericguo/>
-procedures : Form1.cs clustering algorithm is applied DBSCAN (Density-Based Spati cal Clustering of Application with Noise) example, two parameters can EPS and MinPts regulation clustering. DBSCAN.cs is, the clustering algorithm further information please refer to the "data mining" or books related data clustering example from sxdb.m db, an Access database. Known issues and recommendations for further improvement : : 64 dbscan.cs OK, SortedList not support duplicate keys, and therefore if two data points from the same pool can not be solved by adding : By applying an artificially reduce a small amount of data from different points without clustering results on the impact of a solution of the problem : double.Epsilon small decrease in the amount of helplessness to make that 2:00 S Platform: |
Size: 26624 |
Author:Huang Yi |
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Description: 一个基于K均值聚类的ＲＢＦ神经网络，注释写的很明白，有不明白的地方可以发邮件问我。-a K-means clustering based on the RBF neural network, notes written very well, did not understand the local mail can ask me. Platform: |
Size: 2048 |
Author:bruce |
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Description: k-means聚类算法在二维平面上的可视化实现
聚类时可以设置类数和迭代阈值
聚类结果用色彩和类圆清楚的表现出来-k-means clustering algorithm in a two-dimensional plane with the Visualization of clustering can be set up several categories and iterative threshold Clustering results using color and class round clearly demonstrated Platform: |
Size: 45056 |
Author:周黎明 |
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Description: 基于c语言实现的聚类算法，运行与visual c++环境下，可以实现均值聚类。-c language based on the clustering algorithm, the operating environment with visual c, means clustering can be achieved. Platform: |
Size: 246784 |
Author:王莹 |
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Description: 基于OpenCV的K-均值算法,用于聚类分析,如图象的颜色聚类,生成相关的颜色表等.-the K-means algorithm for clustering analysis, such as image color clustering, Formation of color, and so on. Platform: |
Size: 2048 |
Author:guolijun |
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Description: k-means聚类算法的java代码实现,良好的代码风格，适合扩充-k-means clustering algorithm to achieve the java code, the code of good style, suitable for expansion Platform: |
Size: 4096 |
Author: |
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