Agglomerative and K-means Clustering on US Crime Data Objective: Group US states based on crime data using K-means clustering algorithm and then compare the results with Hierarchical Agglomerative Clustering algorithm. Data Source: 50 US states crime dataset will be used. The Data This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also […]
K-means Clustering
K-means Clustering with University Data K-means Clustering is an unsupervised algorithm used to categorise data into groups. The approach followed in the algorithm is as follows: Decide the number of clusters i.e. K. The approach to calculate the ideal value of K is covered further down Set K number of centroids which will be considered as the centre of the K-number of clusters. Note, the […]