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Description: KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
algorithm to set the centres of a cluster model. The matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
Platform: |
Size: 1926 |
Author: 西晃云 |
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Description: 模式识别算法
k均值和感知器算法的具体实现实例-Pattern recognition algorithm for k-means algorithm and the perceptron realize specific examples
Platform: |
Size: 177152 |
Author: fengyuan |
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Description: KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
algorithm to set the centres of a cluster model. The matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
Platform: |
Size: 2048 |
Author: 西晃云 |
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Description: 这是K均值算法,采用c语言编写,K的取值为2,大家可以改变K的值来进行测试-This is the K-means algorithm, using c language, K value of 2, we can change the value of K for testing
Platform: |
Size: 320512 |
Author: Gang Li |
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Description: 不错的GM_EM代码。用于聚类分析等方面。- GM_EM- fit a Gaussian mixture model to N points located in n-dimensional
space.
Note: This function requires the Statistical Toolbox and, if you wish to
plot (for k = 2), the function error_ellipse
Elementary usage:
GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of
clusters. Algorithm follows steps outlined in Bishop
(2009) Pattern Recognition and Machine Learning , Chapter 9.
Additional inputs:
bn_noise- allow for uniform background noise term ( T or F ,
default T ). If T , relevant classification uses the
(k+1)th cluster
reps- number of repetitions with different initial conditions
(default = 10). Note: only the best fit (in a likelihood sense) is
returned.
max_iters- maximum iteration number for EM algorithm (default = 100)
tol- tolerance value (default = 0.01)
Outputs
idx- classification/labelling of data in X
mu- GM centres
Platform: |
Size: 3072 |
Author: 朱魏 |
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Description: KMEANS Trains a k means cluster model CENTRES KMEANS(CENTRES,()
Platform: |
Size: 1024 |
Author: rrogzzms
|
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Description: KMEANS Trains a k means cluster model CENTRES KMEANS(CENTRES,()
Platform: |
Size: 1024 |
Author: Abelit |
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