# Articles - Multivariate Analysis

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## HCPC - Hierarchical Clustering on Principal Components: Essentials

Clustering is one of the important data mining methods for discovering knowledge in multivariate data sets. The goal is to identify groups (i.e. clusters) of similar objects within a data... [Read more]

## FAMD - Factor Analysis of Mixed Data in R: Essentials

Factor analysis of mixed data (FAMD) is a principal component method dedicated to analyze a data set containing both quantitative and qualitative variables (Pagès 2004). It makes it possible... [Read more]

## MCA - Multiple Correspondence Analysis in R: Essentials

The Multiple correspondence analysis (MCA) is an extension of the simple correspondence analysis (chapter @ref(correspondence-analysis)) for summarizing and visualizing a data table containing... [Read more]

## CA - Correspondence Analysis in R: Essentials

Correspondence analysis (CA) is an extension of principal component analysis (Chapter @ref(principal-component-analysis)) suited to explore relationships among qualitative variables (or... [Read more]

## PCA - Principal Component Analysis Essentials

Principal component analysis (PCA) allows us to summarize and to visualize the information in a data set containing individuals/observations described by multiple inter-correlated quantitative... [Read more]

## DBSCAN: Density-Based Clustering Essentials

By , The in Advanced Clustering
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]

## Model Based Clustering Essentials

By , The in Advanced Clustering
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]

## Fuzzy Clustering Essentials

By , The in Advanced Clustering
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: Optimize Clusters

By , The in Advanced Clustering
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]

## Cluster Validation Statistics: Must Know Methods

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]

## Divisive Hierarchical Clustering Essentials

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]

## Heatmap - Static and Interactive: Absolute Guide

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]