2.2 Clustering using k-means algorithm.
Budget: $30 – $250 USD
Input: Random set of N points (millions) is n-dimensional space. k – number of
clusters
Output: Testing results.
http://www.eecs.northwestern.edu/~wkliao/Kmeans/index.html
There are two stages: calculating distances from all points to all centroids
(embarrassingly parallel) and finding new centroids. The biggest question here is how
to optimally
or any moderate project (60 pt) from the below pdf
clusters
Output: Testing results.
http://www.eecs.northwestern.edu/~wkliao/Kmeans/index.html
There are two stages: calculating distances from all points to all centroids
(embarrassingly parallel) and finding new centroids. The biggest question here is how
to optimally
or any moderate project (60 pt) from the below pdf