Articles - Advanced Clustering

This part presents advanced clustering techniques, including: hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and density-based clustering.

Advanced clustering methods

Hierarchical k-means clustering. The hierarchical k-means clustering is an hybrid approach for improving k-means results.

Fuzzy clustering

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.

Model-based clustering

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.

DBSCAN: 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.

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