I don't know how to set forty centers as in discribed as below, so i nee d help with it please!
1 次查看(过去 30 天)
显示 更早的评论
1 partitions 20000 input points into 40 clusters. The dimensions of the data being tested are 24, 32, and 40, respectively. In Experiment 1, input points were generated as follows: *for each of the mentioned dimension, set the forty centers as {(⎯1, –1, …, –1), (–0.95, –0.95, …, –0.95), (–0.9, ⎯0.9, …, –0.9), (–0.85, –0.85, …, –0.85), (0.9, 0.9, …, 0.9), (0.95, 0.95, …, 0.95)}.* Then, use Gaussian distribution with mean μ = 0 and standard deviation σ = 0.05 to generate 500 points around each center. The 40 initial centers to run Stage I of the Kmeans are chosen randomly from data points.
0 个评论
采纳的回答
Walter Roberson
2014-2-10
Supposing you have a num_samples by num_measurements array of coordinates, then
trial_centers = repmat((-1:.05:0.95).', 1, num_measurements);
You can now proceed to generate 500 points around each center. That will get you 500 * 40 = 20,000 multi-dimensional points.
Now out of those 20,000 multi-dimensional points, select 40 of them at random and use those 40 as the initial centers for kmeans.
更多回答(0 个)
另请参阅
类别
在 Help Center 和 File Exchange 中查找有关 Statistics and Machine Learning Toolbox 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!