The document describes the k-means++ seeding algorithm for initializing k-means clustering. It presents the k-means++ algorithm, provides an implementation in MLDemos, and evaluates it on test and real datasets. The results show k-means++ yields a significant reduction in clustering error compared to random initialization, providing better separation of clusters. However, the document also notes there are many seeding techniques and some may work better than k-means++ for certain datasets.