Statistical cluster analysis is a Exploratory Data Analysis Technique which groups heterogeneous objects (M.D.) into homogeneous groups. We will learn the basics of cluster analysis with mathematical way. Note: Result of both the approaches are displayed through the dendrogram tree. Hierarchical cluster analysis ends here, in the next tutorial article I will explain Non-Hierarchical cluster analysis. Till then stay tuned and keep visiting for learning tutorials which you won't get anywhere.

K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the algorithm just tries to find patterns in the data. In'k' means clustering, we have the specify the number of clusters we want the data to be grouped into. The algorithm randomly assigns each observation to a cluster, and finds the centroid of each cluster. These two steps are repeated till the within cluster variation cannot be reduced any further.

Think about the data that you are trying to cluster with. How many dimensions are you using? Are the variables highly related? DO the variables have different standard deviations? For instance, if your data is log-normal then a lot of the cases will be in the low end of the distribution with a few at the high end.

Data-based classification is fundamental to most branches of science. While recent years have brought enormous progress in various areas of statistical computing and clustering, some general challenges in clustering remain: model selection, robustness, and scalability to large datasets. We consider the important problem of deciding on the optimal number of clusters, given an arbitrary definition of space and clusteriness. We show how to construct a cluster information criterion that allows objective model selection. Differing from other approaches, our truecluster method does not require specific assumptions about underlying distributions, dissimilarity definitions or cluster models. Truecluster puts arbitrary clustering algorithms into a generic unified (sampling-based) statistical framework. It is scalable to big datasets and provides robust cluster assignments and case-wise diagnostics. Truecluster will make clustering more objective, allows for automation, and will save time and costs. Free R software is available.

The idea behind hierarchical cluster analysis is to show which of a (potentially large) set of samples are most similar to one another, and to group these similar samples in the same limb of a tree. Each of the samples can be thought of a sitting in an m-dimensional space, defined by the m variables (columns) in the dataframe. We define similarity on the basis of the distance between two samples in this m-dimensional space. Several different distance measures could be used, but the default is Euclidean distance and this is used to work out the distance from every sample to every other sample. This quantitative dissimilarity structure of the data is stored in a matrix produced by the "dist function".