[ACM Press the 19th international confer
โ
Sculley, D.
๐
Article
๐
2010
๐
ACM Press
๐
English
โ 265 KB
We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency, scalability, and sparsity encountered in user-facing web applications. First, we propose the use of mini-batch optimization for k-means clustering. This reduces computation cost