Articles - Unsupervised machine learning

DBSCAN (Density-Based Spatial Clustering and Application with Noise), is a density-based clusering algorithm, introduced in Ester et al. 1996, which can be used to identify clusters of any shape... [Read more]

The traditional clustering methods, such as hierarchical clustering (Chapter @ref(agglomerative-clustering)) and k-means clustering (Chapter @ref(kmeans-clustering)), are heuristic and are not... [Read more]

The fuzzy clustering is considered as soft clustering, in which each element has a probability of belonging to each cluster. In other words, each element has a set of membership coefficients... [Read more]

Hierarchical K-Means Clustering K-means (Chapter @ref(kmeans-clustering)) represents one of the most popular clustering algorithm. However, it has some limitations: it requires the user to... [Read more]

Clusters can be found in a data set by chance due to clustering noise or sampling error. This article describes the R package pvclust (Suzuki and Shimodaira 2015) which uses bootstrap resampling... [Read more]

Choosing the best clustering method for a given data can be a hard task for the analyst. This article describes the R package clValid (Brock et al. 2008), which can be used to compare... [Read more]

The term cluster validation is used to design the procedure of evaluating the goodness of clustering algorithm results. This is important to avoid finding patterns in a random data, as well as,... [Read more]

Determining the optimal number of clusters in a data set is a fundamental issue in partitioning clustering, such as k-means clustering (Chapter @ref(kmeans-clustering)), which requires the user... [Read more]

Before applying any clustering method on your data, it’s important to evaluate whether the data sets contains meaningful clusters (i.e.: non-random structures) or not. If yes, then how many... [Read more]

The divisive hierarchical clustering, also known as DIANA (DIvisive ANAlysis) is the inverse of agglomerative clustering (Chapter @ref(agglomerative-clustering)). This article introduces the... [Read more]

A heatmap (or heat map) is another way to visualize hierarchical clustering. It’s also called a false colored image, where data values are transformed to color scale. Heat maps allow us to... [Read more]

As described in previous chapters, a dendrogram is a tree-based representation of a data created using hierarchical clustering methods (Chapter @ref(agglomerative-clustering)). In this article,... [Read more]