“…), leading to the development of many techniques that fall outside the classics studied in statistical textbooks. Examples of this are the simple‐centers algorithm (Cobo, López‐Herrera, Herrera‐Viedma, & Herrera, ; Coulter, Monarch, & Konda, ; Muñoz‐Leiva, Viedma‐del‐Jesús, Sánchez‐Fernández, & López‐Herrera, ), streamer (Kandylas, Upham, & Ungar, ), and spectral clustering (Chen, Ibekwe‐SanJuan, & Hou, ), among others (Callon, Courtial, & Laville, ). Classical clustering techniques also have a solid background in bibliometrics, as is the case with hierarchical clustering (Kopcsa & Schiebel, ; Leydesdorff, ; McCain, ; Zong et al., ) and k ‐means clustering (Bassecoulard, Lelu, & Zitt, ; Chang, ; Janssens, Leta, Glänzel, & De Moor, ).…”