Principal component analysis with successive interval in K-Means Cluster Analysis (Study case: Poverty data 2013 in East Nusa Tenggara)

K-Means Cluster is a cluster analysis for continuous variables with the concept ofdistance used is a euclidean distancewhere that distance is used as observation variables, which are uncorrelated with each other. The case with the type of data that is correlated categorical can be solved by making categorical data into numerical data by the method called the successive interval and then used Principal Component Analysis. Applied this method in poverty data of East Nusa Tenggara Province in K-Means clusterobtained that Principal Component Analysis with Successive interval obtained variables that take effect to the cluster formation are toilet, fuel, and job.