K-Means Clustering
Visual Clustering

Cluster analysis is a set of mathematical techniques for partitioning a series of data objects into a smaller amount of groups, or clusters, so that the data objects within one cluster are more similar to each other than to those in other clusters.

Miner3D provides the popular K-means method of clustering. K-Means Clustering and K-Means Data Reduction give you more power and more options to process large data sets.

K-means can be used either for clustering data sets visually in 3D or for row reduction and compression of large data sets.

Miner3D’s implementation of K-Means uses a high-performance proprietary scheme based on filtering algorithms and multidimensional binary search trees.

K-means clustering is only available in Miner3D Enterprise and Miner3D Developer packages.