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In this study, there are several things that can describe the socio-economic conditions of farmers having differences in income, education, and health levels, among others: 1. The highest level of income is in Siosar Suka Meriah Village, because they receive government assistance in the form of land, funds , subsidized fertilizers, and seeds, while in Kuta Tengah Village Shelter the income is lower because the government only provides temporary shelter. The lack of capital also caused the lack of income and land to not allow farming because it was located on the slopes of Mount Sinabung. 2. The education level in Siosar Suka Meriah Village is lower, because after moving to relocation the distance to school is too far and some children do not want to continue their education. Whereas in Kuta Tengah Village Shelter their education is higher, because the distance to school is not too far away and most children also have the desire to go to school. 3. The health level in Siosar Suka Meriah Village and the level of health in the Kuta Tengah Village Shelter together use BPJS / KIS which is programmed by the government for the Mount Sinabung eruption refugee community.

Marketplace is one of the most popular digital business in Indonesia. One of the category that grow in Marketplace X is shops that sell baby equipment or better known as babyshop. In order to provide the best service and keep the credibility of babyshop specialty shops, important to do qualitry monitoring on of them through clustering. Clustering based on store reputation assessment indicators consisting of variables that are categorical and numerical in scale. This study aims to classify babyshop on Marketplace X based on the characteristics of the store using cluster analysis with cluster ensemble based mixed data clustering (CEBMDC) based on the weigthed squeezer algorithm. This study use the stream data from 218 babyshop at Marketplace X which consists of service factors, reputation level, type and location of the babyshop. The optimal cluster results was into three clusters in which cluster one consists of 21% babyshop, cluster two 48% babyshop, and 31% babyshop at cluster three. The first cluster is a cluster with the tendency of the babyshop to be classified as good, the cluster two tend to have a normal (neutral) reputation, while members of cluster three has a for poor reputation.