This course presents advanced clustering techniques, including: hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and density-based clustering.
The density-based clustering (DBSCAN is a partitioning method that has been introduced in Ester et al. (1996). It can find out clusters of different shapes and sizes from data containing noise and outliers. In this chapter, we’ll describe the DBSCAN algorithm and demonstrate how to compute DBSCAN using the fpc R package. In model-based clustering, the data are viewed as coming from a distribution that is mixture of two ore more clusters. It finds best fit of models to data and estimates the number of clusters. In this chapter, we illustrate model-based clustering using the R package mclust. Fuzzy clustering is also known as soft method. Standard clustering (K-means, PAM) approaches produce partitions, in which each observation belongs to only one cluster. This is known as hard clustering. In Fuzzy clustering, items can be a member of more than one cluster. Each item has a set of membership coefficients corresponding to the degree of being in a given cluster. In this article, we’ll describe how to compute fuzzy clustering using the R software. This current article presents the fuzzy c-means clustering algorithm. This article describes how to compute the fuzzy clustering using the function cmeans() [in e1071 R package] The hierarchical k-means clustering is an hybrid approach for improving k-means results. In this article, you will learn how to compute hierarchical k-means clustering in R