I\'m trying to figure out the best practice for implementing a complex algorithm on stored information in a relational DB.
I\'m playing a little bit with image similarity. In fact i\'m playing with image retrieval system. Ideally i wanna to create some kind of image index that I can query to get similar images.
I am trying to do some (k-means) clustering on a very large matrix. The matrix is approximately 500000 rows x 4000 cols yet very sparse (only a c开发者_JAVA技巧ouple of \"1\" values per row). I want
I recently started experimenting with the bigan开发者_高级运维alytics package for R. I ran into a problem however...
This is a question about k-means clustering algorithm. I have the following points and clustering of data S1. Can anyone tell 开发者_开发技巧me how to calculate the total error associated with this cl
I would like to know simple k-means algorithm in java. I want to use k-means only for grouping one d开发者_如何学编程imensional array not multi.
I\'m using function cvKMeans2() from OpenCV library for clustering. It has optional parametr: centers - The optional output array of the cluster centers
I\'ve been studying about k-means clustering, and one thing that\'s not clear is how you choose the value of k.Is it just a matter of trial and error, or is there 开发者_如何学JAVAmore to it?You can m
I\'m using Opencv\'s K-means implementation to cluster a large set of 8-dimensional vectors. They cluster fine, but I can\'t find any way to see the prototypes created by the clustering process. Is th
加载中,请稍侯......