K-Means Clustering

Free

1.6for iPhone, iPad

Donald Schaefer

Developer

Sep 27, 2021

Update Date

Education

Category

App Store

Age Rating

Apps in this category do not contain restricted content.

Apps in this category may contain mild or occasional cartoon, fantasy or real-life violence, as well as occasional or mild adult, sexually suggestive or horrifying content and may not be suitable for children under 9 years of age.

12+

Apps in this category may contain occasional mild indecent language, frequent or intense cartoon or real-life violence, minor or occasional adult or sexually suggestive material, and simulated gambling, and may be for children under 12 years of age.

17+

You must be at least 17 years old to access this App.

Apps in this category may contain frequent and intense offensive language; Frequent and intense cartoon, fantasy or realistic violence: frequent and intense adult, scary and sexually suggestive subjects: as well as sexual content, nudity, tobacco, alcohol and drugs, may not be suitable for children under 17 years of age.

K-Means Clustering Screenshots

iPad

iPhone

About K-Means Clustering

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.