K-MEANS FULL APPLICATION, PERFORMANCE
K-MEANS FULL APPLICATION, PERFORMANCE Kmeans we generally use for outliear mining Pre processing Visualization Sampling (for, eg, you need 10000 samples from 1 million data set, an excellent approach will be make 10000 cluster and take a single data point from it) Kmeans is a partional clustering Evaluation:- 1.Diameter- avg pairwise distance in each cluster 2.Radius -Avg distance of centroid to it’s data points in cluster
K-MEANS FULL APPLICATION, PERFORMANCE K-MEANS FULL APPLICATION, PERFORMANCE Kmeans we generally use for outliear mining Pre processing Visualization Sampling (for, eg, you need 10000 samples from 1 million data set, an excellent approach will be make 10000 cluster and take a single data point from it) Kmeans is a partional clustering Evaluation:- 1.Diameter- avg pairwise distance in each cluster 2.Radius -Avg distance of centroid to it’s data points in cluster data-science
KMEANS FULL APPLICATION PERFORMANCE KMEANS FULL APPLICATION PERFORMANCE Kmeans generally use outliear mining Pre processing Visualization Sampling eg need 10000 sample 1 million data set excellent approach make 10000 cluster take single data point Kmeans partional clustering Evaluation 1Diameter avg pairwise distance cluster 2Radius Avg distance centroid it’s data point cluster datascience