K-Means Clustering 1.4

K-means clustering partitions data into k mutually exclusive clusters, and returns the index of the cluster to which it has assigned each observation.

K-means clustering is popular for cluster analysis in data mining. K-Means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

The K-Means Clustering Ipad app provides a tap method entry of 1-20 data points with a selection of 1-5 Clusters for the allocation of the data points. This app also provides the summary of the Cluster/Data Points with assigned PointX/PointY values and a calculation of the Center Point for each cluster.

The K-Means Clustering app displays the Clusters/Data Points with a color coding methodology for each data point.

A Data Entry component which provides for the manual entry of [x,y] Data Points and a results Data Table which displays the [x,y] Data Points and the computed cluster for the Data Points.