Steps of k-mean algorithm -K-Means Clustering algorithm is an idea, in which there is need to classify the given data set into K clusters, the value of K (Number of clusters) is defined by the user which is fixed. Euclidean Distance is used for calculating the distance of data point from the particular centroid.

This algorithm consists of four steps: 1. Initialization In this first step data set, number of clusters and the centroid that we defined for each cluster. 2. Classification The distance is calculated for each data point from the centroid and the data point having minimum distance from the centriod of a cluster is assigned to that particular cluster. 3. Centroid Recalculation Clusters generated previously, the centriod is again repeatly calculated means recalculation of the centriod. 4. Convergence Condition Some convergence conditions are given as below: 4.1 Stopping when reaching a given or defined number of iterations. 4.2 Stopping when there is no exchange of data points between the clusters. 4.3 Stopping when a threshold value is achieved. 5. If all of the above conditions are not satisfied, then go to step 2 and the whole process repeat again, until the given conditions are not satisfied Agglomerative Hierarchical Algorithm For n samples, agglomerative algorithms begin with n clusters and each cluster contains a single sample or a point. Then two clusters will merge so that the similarity between them is the closest until the number of clusters becomes 1 or as specified by the user. 1. Start with n clusters, and a single sample indicates one cluster. 2. Find the most similar clusters Ci and Cj then merge them into one cluster. 3. Repeat step 2 until the number of cluster becomes one or as specified by the user. The distances between each pair of clusters are computed to choose two clusters that have more opportunity to merge. There are several ways to calculate the distances between the clusters Ci and Cj.

Hi Hasan, if you are comparing the results of two different clustering methods the cluster_similarity function in the ‘clusteval’ R pakage may help. If you have tried another method that you think is better suited but your code is not working you may think of posting it over the R community of stackoverflow or reddit to get help in debugging it ... or you can try posting it here although I am not sure this can be considered code friendly ...

Best,

Mar

Can you post your R code that you have used thus far?

Did you use hclust() for hierarchical clustering and kmeans() for k means clustering in base R ?

For calculating cluster similarities the R package fpc comes to my mind.

There, cluster.stats() is a method for comparing the similarity of two cluster solutions using a lot of validation criteria (Hubert's gamma coefficient, the Dunn index and the corrected rand index)

I would suggest using Adjusted rand index. I think this post explains it nicely:

https://stats.stackexchange.com/questions/230838/is-this-big-difference-meaningful-63-rand-index-but-0-004-adjusted-rand-index

It is available in several R packages, including mclust, blockmodeling, e1071.

cluster.stat function in R coding