Billard L., Diday E. Clustering Methodology for Symbolic Data

Hoboken: Wiley, 2020. — 345 p.The theme of this volume centers on clustering methodologies for data which allow observations to be described by lists, intervals, histograms, and the like (referred to as “symbolic” data), instead of single point values (traditional “classical” data). Clustering techniques are frequent participants in exploratory data analyses when the goal is to elicit identifying classes in a data set. Often these classes are in and of themselves the goal of an analysis, but they can also become the starting point(s) of subsequent analyses. There are many texts available which focus on clustering for classically valued observations. This volume aims to provide one such outlet for symbolic data.