Application of Factor Analysis to k-means Clustering Algorithm on Transportation Data

Abstract

Factor Analysis is a very useful linear algebra technique used for dimensionality reduction. It is also used for data compression and visualization of high dimensional datasets. This technique tries to identify from among a large set of variables, a reduced set of components which summarizes the original data. This is done by identifying groups of variables which have a strong inter correlation. The original variables are transformed into a smaller set of components which have a strong linear correlation. Using several data analysis techniques like Principal Components Analysis (PCA), Factor Analysis, cluster analysis may give insight into the patterns present in the data but may also give different results. The aim of this work is to study the use of Factor Analysis (FA) in capturing the cluster structures from transportation (HIS) data. It is proposed to compare the clustering obtained from original data from that of factor scores. Steps involved in preprocessing the transportation data are also illustrated.

Carbon Footprints and Consumer Lifestyles: An Analysis of Lifestyle Factors and Gap Analysis by Consumer Segment in Japan

“…Other compelling applications of cluster analysis include a study of dietary habits in Canada [34] and Hungary [35], and household appliances in Germany [36], but their scope is limited to particular consumption domains rather than being applicable to the environmental impact of lifestyles as a whole. Combining these two methods of factor analysis and cluster analysis was an approach taken by a previous comparative study, which concluded that using the results of exploratory factor analysis as inputs for cluster analysis improves the quality of clusters [37]. Drawing on this conclusion, the present study thus combines cluster analysis and exploratory factor analysis to examine variations in carbon footprints within a country based on the underlying lifestyle factors.…”

Implications of COVID-19 policies on the sustainable corporate practices of European firms

“…For clarity of reference, factor categories and clusters are directly presented in the following section. Since several studies found benefits in combining cluster and factor analyses (Anand et al. , 2014; Gorman and Primavera, 1983; Visbal-Cadavid et al.…”

“…For clarity of reference, factor categories and clusters are directly presented in the following section. Since several studies found benefits in combining cluster and factor analyses (Anand et al, 2014;Gorman and Primavera, 1983;Visbal-Cadavid et al, 2020), the output of these two analyses was studied together in this paper to understand the integrated dynamics of macroeconomic and policy measures within country groups. Differences across years and clusters were assessed, and policy influences and variations in the resulting cluster "rankings" were verified.…”

Identifying the perspectives for sustainability enhancement

“…The benefit of applying this technique is that when the data is reoriented, the first few dimensions obtained show maximum variance, i.e. they give the maximum amount of information as reflected by their respective eigenvalues wherein each principal component resembles a linear combination of the original data (Anand et al , 2014). Thus, the fundamental principle for applying PCA is to help visualize complex data sets by eliminating unnecessary dimensions which do not add much value to the data that is present.…”