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The large amount of data available for analysis and management raises the need for defining, determining, and extracting meaningful information from the data. Hence in scientific, engineering, and economics studies, the practice of clustering data arises naturally when sets of data have to be divided into subgroups with the aim of possibly deducting common features for data belonging to the same subgroup. For instance, the innovation scoreboard  (see Figure 1) allows for the classification of the countries into four main clusters corresponding to the level of innovation defining the “leaders,” the “followers,” the “trailing,” and the “catching up” countries. Many other disciplines may require or take advantage of a clustering of data, from market research  to gene expression analysis , from biology to image processing . Therefore, several clustering techniques have been developed (for details see “Review of Clustering Algorithms”).