To achieve scientific progress in terms of building a cumulative body of knowledge, careful
attention to benchmarking is of the utmost importance, requiring that proposals of new …

Cluster analysis comprises of several unsupervised techniques aiming to identify a
subgroup (cluster) structure underlying the observations of a data set. The desired cluster …

This paper synthesizes the results, methodology, and research conducted concerning the K‐
means clustering method over the last fifty years. The K‐means method is first introduced …

Often considered more as an art than a science, the field of clustering has been dominated
by learning through examples and by techniques chosen almost through trial-and-error …

I am very happy to have this opportunity to present the work of Boris Mirkin, a distinguished
Russian scholar in the areas of data analysis and decision making methodologies. The …

A new procedure is proposed for clustering attribute value data. When used in conjunction
with conventional distance-based clustering algorithms this procedure encourages those …

Linear and nonlinear multivariate statistical analyses are applied to determine how the types
of accidents that occur on heavily used freeways in Southern California are related both to …

L'analyse des données textuelles (ADT) permet d'explorer et de visualiser les recueils de
textes les plus divers: œuvres littéraires, transcriptions d'entretien, discours politiques …

The Gifi system of analyzing categorical data through non-linear varieties of classical
multivariate analysis techniques is reviewed. The system is characterized by the optimal …

In this paper, we present evidence of strong relationships between traffic flow conditions and
the likelihood of traffic accidents (crashes), by type of crash. Traffic flow variables are …