Beyond tandem analysis: Joint dimension reduction and clustering in R

We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and reduced K-means; both methods combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means, i-FCB and cluster correspondence analysis, which combine multiple correspondence analysis with K-means. Two examples on real data sets are provided to illustrate the usage of the main functions

Incremental visualization of categorical data

The general aim of data reduction (DR) is to synthesize the information within a data set by defining a set of homogeneous groups of observations (row-wise) and a set of linear combinations of the starting attributes that approximate their relationship structure (columnwise). That is, DR embeds clustering and dimension reduction techniques that are often used sequentially. Albeit such sequential approach is straightforward, dimension reduction is applied first, and the reduced-space observation projections are clustered together, it may fail in retrieving the structure underlying data. In fact, the low-dimensional solution may mask the groups of homogeneous observations. To overcome this problem, joint DR techniques have been proposed, in this paper we focus on the categorical data case and on how such approaches relates to the explained heterogeneity

Analysis of young people neither in employment nor in education and training: A fuzzy mca based approach

Y oung adults in Neither in Employment nor in Education and Training (NEET) are at high risk of adverse health outcome, in particular of mental health problems. The aim of this study is to identify the symptomatological profiles of young Italian NEETs. The data set in question consists of 150 Italian respondents to the Adult Self Report (ASR 18-59) survey for assessing the mental health problems. A two-step unsupervised learning approach that involves fuzzy multiple correspondences analysis and clustering is applied to identify different symptomatological profiles of NEETs-related problems. The obtained results are compared to a principal component analysis-based approach. Finally, clinical implications in psychological practices are discussed